An adaptive projection navigation method and system based on an outdoor environment

Abstract

The application provides an outdoor environment-based adaptive projection navigation method and system, the method comprising: collecting environment features and ground texture features of an outdoor environment; determining a projection interference level of the outdoor environment according to the environment features; constructing a basic projection scheme of the outdoor environment according to the ground texture features; completing initial parameter calibration of a projection module; generating dynamic navigation content, performing anti-interference processing on the dynamic navigation content in combination with the projection interference level to obtain navigation projection content; and projecting the navigation information to the ground in front of a user according to the calibrated projection parameters by the projection module. The application projects the navigation content on the ground in front of the user, effectively reduces the interference of sight deviation on the safety of travel, enables the user to easily and timely obtain key navigation information, fully utilizes the intuitive and convenient information output advantage of the projection technology, and accurately matches the dynamic nature and environmental complexity requirements of outdoor navigation.

Description

Technical Field

[0001] This application relates to the field of outdoor navigation, and more specifically, to an adaptive projection navigation method and system based on the outdoor environment. Background Technology

[0002] In outdoor travel scenarios, navigation technology is a key support for ensuring travel efficiency and safety. Existing outdoor navigation solutions mainly rely on the screen of handheld terminals or wearable devices to display navigation information, conveying route guidance to users through text, icons, and other forms, which has become the mainstream navigation implementation method.

[0003] However, existing screen-based outdoor navigation solutions have many inherent drawbacks and are difficult to adapt to the complex needs of outdoor travel: First, users need to frequently look down at the screen to obtain navigation information, which leads to distraction and increases the risk of collisions with the surrounding environment, posing significant safety hazards; Second, screen displays are greatly affected by the outdoor environment. Under strong direct sunlight, reflections and glare are likely to occur, and in low-light environments, the screen brightness is insufficient, both of which make it difficult to recognize navigation information and result in poor environmental adaptability; Third, the screen display range is limited, and navigation information is disconnected from the user's field of vision. Users need to repeatedly switch between screen information and the actual environment for comparison, resulting in low path matching efficiency and a high risk of navigation deviation. Summary of the Invention

[0004] To address the problems existing in current technologies, this application provides an adaptive projection navigation method and system based on outdoor environments. The specific solution is as follows:

[0005] An adaptive projection navigation method based on outdoor environment, comprising:

[0006] The multimodal perception module collects environmental features and ground texture features of the outdoor environment;

[0007] The scattering coefficient and attenuation coefficient of light are calculated based on the environmental characteristics to determine the projection interference level of the outdoor environment; feature extraction and classification are performed on the ground texture features to obtain feature labels, and a basic projection scheme for the outdoor environment is constructed based on the feature labels.

[0008] The basic projection scheme is converted into parameter configuration instructions and sent to the projection module worn by the user to complete the initial parameter calibration of the projection module.

[0009] Dynamic navigation content is generated based on navigation path planning results and real-time positioning data. Anti-interference processing is then performed on the dynamic navigation content in conjunction with the projection interference level to obtain navigation projection content. The anti-interference processing includes backscattering correction and projection content dot matrix redundancy encoding.

[0010] The navigation projection content is converted into content display instructions and sent to the projection module. The projection module then projects the navigation information onto the ground in front of the user based on the calibrated projection parameters.

[0011] In some specific embodiments, it also includes:

[0012] The system collects positioning data and dynamic environmental data in real time during navigation, updates dynamic navigation content based on the positioning data, and issues content display instructions. If changes in dynamic environmental data cause the projection interference level to exceed a preset threshold, the system regenerates the projection parameter configuration scheme and issues parameter adjustment instructions to achieve differentiated dynamic updates of projection parameters and navigation content, thus completing adaptive navigation projection.

[0013] In some specific embodiments, when calculating the scattering coefficient, the concentration of air particulate matter and the air humidity are used as input parameters, and the wavelength-related scattering coefficient is obtained by substituting them into the Mie scattering model; when calculating the attenuation coefficient, the light intensity and the concentration of air particulate matter are used as input parameters, and the transmission attenuation coefficient of light in the current environment is calculated based on the Lambert-Beer law; the projection interference level is determined by calculating the combined value of the scattering coefficient and the attenuation coefficient through a preset weighted formula, and then comparing it with the preset low, medium and high threshold values.

[0014] In some specific embodiments, the environmental features include light intensity, air particulate matter concentration, and air humidity; the ground texture features include ground roughness, flatness, reflectivity, and texture color distribution data.

[0015] In some specific embodiments, the multimodal sensing module includes a light sensor, a particulate matter sensor, a humidity sensor, an image acquisition module, and a laser ranging module; wherein, the light sensor, particulate matter sensor, and humidity sensor are used to collect environmental features, and the image acquisition module and the laser ranging module cooperate to collect ground texture features.

[0016] In some specific embodiments, the process of obtaining the basic projection scheme includes:

[0017] The gray-level co-occurrence matrix algorithm is used to extract the contrast, correlation, energy and uniformity features of the ground texture. At the same time, the ground height difference data obtained by laser ranging is combined to form a multi-dimensional ground texture feature set.

[0018] Feature labels are obtained by outputting the multi-dimensional ground texture features through a preset classification model;

[0019] Based on the parameters in the matching mapping table obtained from the classification feature labels, a basic projection scheme adapted to the current ground texture is generated; the mapping table presets corresponding projection brightness threshold, contrast threshold, focal length parameter and initial value of projection angle for different feature labels.

[0020] In some specific embodiments, the feature tags are divided into four categories: strong light reflective type, weak light absorbent type, rough scattering type, and smooth mirror type;

[0021] For projects with strong glare, reduce the projection brightness and increase the contrast in the projection parameters.

[0022] For low-light absorption types, increase the projection brightness and projection power in the projection parameters;

[0023] For coarse scattering type, a short focal length projection mode is adopted in the projection parameters and the projection spot size is reduced;

[0024] For smooth mirror surfaces, adjust the projection angle in the projection parameters and enable the anti-glare filter mode.

[0025] In some specific embodiments, the backscattering correction is implemented as follows:

[0026] Using the scattering coefficient and attenuation coefficient as input parameters, the scattering offset and brightness attenuation value of the projected light in the current environment are calculated through a light transmission simulation model. Based on the scattering offset, the pixel coordinates of the dynamic navigation content are compensated in reverse, and based on the brightness attenuation value, the grayscale value of the navigation content is pre-enhanced to complete the backscattering correction.

[0027] In some specific embodiments, the specific implementation process of the projection content dot matrix redundancy encoding is as follows: the core information of the dynamic navigation content is converted into a standardized dot matrix, and repeated arrangement is performed around the core dot matrix according to the redundancy order; wherein the first-order redundancy encoding is a single mirror repetition of the core dot matrix, and the third-order redundancy encoding is a three-fold staggered repetition of the core dot matrix along the horizontal, vertical and diagonal directions, and the encoded data has a built-in CRC cyclic redundancy check code for data integrity verification after projection.

[0028] An adaptive projection navigation system based on outdoor environment, comprising:

[0029] The data acquisition unit is used to acquire environmental features and ground texture features of the outdoor environment through the multimodal perception module;

[0030] The scheme construction unit is used to calculate the scattering coefficient and attenuation coefficient of light based on the environmental characteristics, and then determine the projection interference level of the outdoor environment; to extract and classify the ground texture features to obtain feature labels, and to construct a basic projection scheme for the outdoor environment based on the feature labels;

[0031] The projection initialization unit is used to convert the basic projection scheme into parameter configuration instructions and send them to the projection module worn by the user to complete the initial parameter calibration of the projection module.

[0032] The projection correction unit is used to generate dynamic navigation content based on navigation path planning results and real-time positioning data, and to perform anti-interference processing on the dynamic navigation content in combination with the projection interference level to obtain navigation projection content; the anti-interference processing includes backscattering correction and projection content dot matrix redundancy encoding.

[0033] The projection output unit is used to convert the navigation projection content into content display instructions and send them to the projection module. The projection module then projects the navigation information onto the ground in front of the user based on the calibrated projection parameters.

[0034] Beneficial Effects: This application proposes an adaptive projection navigation method and system based on outdoor environments. It breaks through the limitations of traditional outdoor navigation relying on handheld devices or wearable screens, projecting dynamic navigation content directly onto the ground in front of the user. This aligns with the user's natural observation habits while walking, eliminating the need to frequently look down at the device or look up to find distant landmarks. This effectively reduces the interference of line-of-sight deviation on walking safety, allowing users to easily and promptly obtain key navigation information such as turning directions and distance prompts. A multimodal perception module comprehensively collects outdoor environmental features and ground texture features, providing accurate data support for subsequent optimization. Based on environmental features, scattering and attenuation coefficients are quantified to determine the projection interference level. Combined with ground texture classification, an adaptive basic projection scheme is constructed, achieving precise matching of projection parameters with the outdoor environment and ground characteristics. Furthermore, anti-interference processing, including backscattering correction and dot matrix redundancy coding, is performed to address the interference level. Combined with initial parameter calibration of the projection module, this effectively overcomes problems such as projection blurring, distortion, and unrecognizable information caused by outdoor light scattering, attenuation, and differences in ground reflection / scattering, ensuring that navigation content is clearly and neatly presented in different outdoor scenarios.

[0035] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of the adaptive projection navigation method of this application;

[0038] Figure 2 This is a schematic diagram illustrating the principle of the adaptive projection navigation method of this application;

[0039] Figure 3This is a schematic diagram of the dynamic navigation process of this application;

[0040] Figure 4 This is a schematic diagram of the process for determining the projection interference level in this application;

[0041] Figure 5 This is a schematic diagram of the adaptive projection navigation system module based on the outdoor environment according to this application.

[0042] Reference numerals in the attached figures: 1-Data acquisition unit; 2-Scheme construction unit; 3-Projection initialization unit; 4-Projection correction unit; 5-Projection output unit. Detailed Implementation

[0043] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0044] This application proposes an adaptive projection navigation method based on outdoor environments. It deeply integrates projection technology with outdoor navigation, breaking the limitations of traditional outdoor navigation that relies on handheld devices or wearable screens. Navigation information is directly presented on the ground in front of the user in projection form, constructing a complete collaborative link. This fully leverages the intuitive and convenient information output advantages of projection technology while precisely matching the dynamic and complex environmental requirements of outdoor navigation. A flowchart of the adaptive projection navigation method is attached. Figure 1 As shown in the attached diagram, the principle is as follows: Figure 2 As shown, the specific solution is as follows:

[0045] An adaptive projection navigation method based on outdoor environment, comprising:

[0046] 101. Collect environmental features and ground texture features of the outdoor environment through a multimodal perception module;

[0047] 102. Calculate the scattering coefficient and attenuation coefficient of light based on environmental characteristics, and then determine the projection interference level of the outdoor environment; extract and classify the features of the ground texture features to obtain feature labels, and construct a basic projection scheme for the outdoor environment based on the feature labels;

[0048] 103. Convert the basic projection scheme into parameter configuration instructions and send them to the projection module worn by the user to complete the initial parameter calibration of the projection module;

[0049] 104. Based on the navigation path planning results and real-time positioning data, dynamic navigation content is generated. Anti-interference processing is performed on the dynamic navigation content in combination with the projection interference level to obtain navigation projection content. The anti-interference processing includes backscattering correction and dot matrix redundancy coding of projection content.

[0050] 105. Convert the navigation projection content into a content display command and send it to the projection module. The projection module then projects the navigation information onto the ground in front of the user based on the calibrated projection parameters.

[0051] Step 101 is the data source construction stage of the entire adaptive projection navigation method. Its core purpose is to provide comprehensive, accurate, and synchronous environmental and ground-based data for all subsequent processing stages, avoiding deviations in the navigation scheme due to missing or incomplete data. Outdoor environmental features are key environmental parameters that directly affect the transmission effect of projected light, encompassing various environmental factors that light may encounter during outdoor propagation. Ground texture features are core ground parameters that determine the final presentation effect of the projected content, reflecting the reflection and scattering characteristics of the ground surface on the projected light. The core advantage of the multimodal perception module lies in its integration of multiple different types of perception modules, enabling simultaneous parallel acquisition of two different dimensions of data, rather than a single module acquiring only one type of data. This multi-dimensional acquisition method ensures data comprehensiveness. It captures both the influence of the environment on light and the ground's ability to present the projection, achieving a complete depiction of the outdoor navigation scenario.

[0052] This step lays a solid data foundation for all subsequent quantitative analysis and scheme design stages. Only based on comprehensive and accurate raw data can the subsequent interference level determination and projection scheme construction accurately adapt to the actual scenario, avoiding problems such as poor projection effect and inaccurate navigation caused by incomplete data from the source, and ensuring the feasibility and reliability of the entire adaptive navigation method.

[0053] Step 102 is the core transition link connecting the raw data and the actual navigation scheme. Essentially, it involves "quantitative analysis + feature simplification" of the two types of raw data collected to form intermediate results that can directly guide subsequent operations. It is divided into two parallel and mutually compatible processing branches.

[0054] The processing logic for environmental characteristics is as follows: various factors in the outdoor environment affect the propagation of projected light through two methods: scattering and attenuation. Scattering causes the direction of light propagation to shift, and attenuation causes the brightness of light to decrease. Both of these phenomena directly affect the clarity of the projected content. By calculating the scattering coefficient and attenuation coefficient through environmental characteristics, the originally abstract environmental interference can be transformed into quantifiable numerical indicators. Then, by using these two quantitative indicators to determine the level of projection interference, it is equivalent to setting an "interference intensity scale" for subsequent anti-interference processing, clarifying the degree of interference that needs to be combated.

[0055] The processing logic for ground texture features is as follows: Ground texture features are complex and diverse. Directly designing projection parameters based on the original texture data will face problems such as high difficulty in parameter matching and low efficiency. Feature extraction can extract key information that reflects the core characteristics of the ground from complex texture data. Then, through classification, these key information are transformed into concise feature labels. Each label corresponds to a type of ground with similar presentation characteristics. The basic projection scheme is built around the feature labels. Essentially, it is to establish the adaptation relationship between "ground type and projection parameters" in advance, so that the projection parameters can quickly match the current ground characteristics and avoid the blindness of parameter design.

[0056] This step, through quantification and simplification, transforms complex outdoor environments and ground conditions into easily manageable intermediate results. This enables subsequent parameter calibration and anti-interference processing to accurately meet the needs of actual scenarios, ensuring that subsequent solutions can both adapt to the presentation characteristics of the ground and specifically combat ambient light interference. This lays the core logical foundation for achieving high-quality projection navigation.

[0057] Step 103's core objective is to ensure the initial operating state of the projection module accurately adapts to the current ground texture features, providing a stable hardware foundation for subsequent projection. The basic projection scheme includes core parameters such as projection brightness, contrast, and focal length adapted to the current ground surface. However, these parameters are "theoretical configuration information" and cannot be directly recognized by the projection module. Therefore, they need to be converted into parameter configuration instructions that the projection module can parse. This conversion process is equivalent to building a "communication bridge" between the software scheme and the hardware device, ensuring that the parameter requirements in the scheme can be accurately transmitted to the projection module. As the final output carrier of navigation information, the projection module's parameter state directly determines the projection effect. Initial parameter calibration involves adjusting the projection module according to the parameters in the instructions, matching the module's operating state with the current ground characteristics. For example, calibrating to a suitable focal length on rough ground to avoid blurry projected content. Simultaneously, the projection module adopts a wearable design, allowing it to adjust its position synchronously with the user's movement, avoiding the drawback of fixed projection devices that cannot follow the user's movement.

[0058] This step achieves precise adaptation between the projection module and the ground characteristics through parameter calibration, avoiding problems such as blurry projection and unsuitable brightness caused by parameter mismatch at the hardware level. The wearable design ensures the synchronization between the projection module and the user, ensuring that subsequent navigation information can always be projected onto the effective area in front of the user, providing hardware guarantee for the accurate presentation of navigation information.

[0059] Step 104 is the navigation content generation and optimization stage. It aims to ensure the real-time performance and accuracy of the navigation content while enhancing its resilience to interference in complex environments, ensuring users can clearly obtain navigation information. The dynamic navigation content generation logic is as follows: the navigation path planning result is the user's overall travel goal, while real-time positioning data reflects the user's current specific location. Combining these two to generate navigation content allows navigation information to match the user's travel status in real time—for example, generating a turn arrow when the user reaches an intersection; generating distance prompts when the user is close to the destination. This dynamic generation method completely solves the problem of lagging traditional fixed navigation information, ensuring that the navigation content always meets the user's current needs. The core logic of interference resistance is "targeted optimization." Based on the projection interference level determined in step 102, two methods are used to optimize the navigation content: backscattering correction is used to offset the impact of ambient light scattering on the projection content, avoiding blurry content edges; projection content dot matrix redundancy coding is used to improve the navigation content's resistance to occlusion and fault tolerance, preventing the loss of key information.

[0060] This step ensures the real-time nature and accuracy of navigation content through dynamic generation, allowing users to obtain navigation information that matches their current travel status in a timely manner. On the other hand, it enhances the adaptability of navigation content in complex outdoor environments through anti-interference processing, ensuring that navigation content remains clearly identifiable even in scenarios with severe light interference, thus avoiding the problem of users being unable to recognize navigation information due to environmental interference.

[0061] Step 105 is the final execution stage of the entire adaptive projection navigation method. Its core objective is to achieve accurate presentation of the optimized navigation content, completing a closed loop from navigation information generation to user perception. The navigation projection content, after anti-interference processing, already possesses the ability to resist environmental interference, but it still needs to be converted into content display instructions executable by the projection module. This conversion process is consistent with the parameter instruction conversion logic in step 103; both establish a communication bridge between software information and hardware execution, ensuring that the optimized navigation content can be accurately transmitted to the projection module. When performing the projection task, the projection module operates according to the projection parameters calibrated in step 103. These calibrated parameters ensure that the projected content presents appropriate brightness and clarity on the current ground, avoiding poor presentation effects due to parameter deviations. Simultaneously, projecting navigation information onto the ground in front of the user aligns with the user's natural observation habits during movement—the user does not need to look down at the device screen or look up to find distant landmarks; they only need to look straight ahead to obtain navigation information, greatly improving the convenience and safety of navigation.

[0062] This step achieves a complete closed loop for navigation information from generation and optimization to presentation. It ensures the clear presentation of navigation content through precise projection, while the projection method that conforms to the user's natural observation habits enhances the navigation experience. Ultimately, it achieves efficient, convenient, and clear adaptive navigation in outdoor environments.

[0063] In this application, the navigation side generates dynamic content based on real-time positioning and path planning, ensuring the timeliness and relevance of navigation information. The projection side optimizes the entire process to effectively present the navigation content. It collects outdoor environmental and ground data through multimodal sensing, quantifies the degree of environmental interference with projection light, classifies ground texture characteristics, and then constructs an appropriate projection parameter scheme and completes module calibration. Furthermore, it performs anti-interference processing based on the interference level corresponding to navigation needs, ensuring clear presentation of dynamic navigation content in complex outdoor environments. Simultaneously, the wearable design of the projection module precisely matches the dynamic nature of navigation, adjusting the projection position synchronously with the user's movement, ensuring that navigation information is always within the user's natural observation range and can be obtained without shifting their line of sight, thus improving navigation convenience and travel safety. This integration not only solves the pain points of traditional outdoor navigation, such as the need to frequently check devices to obtain information and the interference of line of sight with travel safety, but also overcomes the problem that ordinary projection technology is easily affected by light and ground conditions in outdoor environments, resulting in poor presentation. It makes projection technology an efficient output carrier for outdoor navigation, making the transmission of navigation information more direct, clearer, and more adaptable to complex outdoor scenarios. Ultimately, it achieves the synergistic effect of "navigation needs driving projection optimization and projection effects ensuring navigation experience", significantly improving the practicality and reliability of outdoor navigation.

[0064] In some specific embodiments, the method further includes: real-time acquisition of positioning data and dynamic environmental data during navigation; updating dynamic navigation content based on positioning data and issuing content display instructions; if changes in dynamic environmental data cause the projection interference level to exceed a preset threshold, regenerating the projection parameter configuration scheme and issuing parameter adjustment instructions to achieve differentiated dynamic updates of projection parameters and navigation content, thus completing adaptive navigation projection. By real-time acquisition of two types of dynamic data and differentiated processing, the navigation content and projection parameters are always matched with the current actual scene, overcoming the limitations of relying solely on the initial configuration in basic methods. This allows the navigation system to cope with complex and ever-changing outdoor scenarios. For example, situations such as a sudden change from sunny to cloudy weather, entering a dusty area during travel leading to a sharp increase in particulate matter concentration, or traversing road sections with different lighting conditions can all be addressed by real-time updates of navigation content and adjustments to projection parameters, ensuring that navigation information remains accurate and projection effects remain clear. This significantly improves the reliability and user experience of outdoor navigation, and is especially suitable for long-distance, multi-road-condition outdoor travel scenarios. The dynamic navigation process is attached. Figure 3 As shown.

[0065] During navigation, the multimodal perception and positioning modules are continuously activated to collect the user's location data and surrounding dynamic environment data in real time. Location data ensures the real-time nature of the navigation content, while dynamic environment data ensures the adaptability of projection parameters. The processing logic for location data involves comparing real-time location data with the preset navigation path planning results to determine the user's current stage of travel, such as distance to the next turn, whether they have deviated from the planned path, and the remaining distance to the destination. Based on these determinations, the navigation content is dynamically updated. For example, when the user approaches an intersection, the display style and timing of the turn arrows are updated; when the user is close to the destination, the remaining distance prompt is refreshed in real time. The updated dynamic navigation content is then converted into content display commands and sent to the projection module, ensuring that the navigation content always matches the user's current travel needs and avoiding navigation information lag.

[0066] For dynamic environmental data, the system continuously analyzes the collected data, recalculates the scattering and attenuation coefficients of light, and then determines whether the current projection interference level exceeds a preset threshold. This preset threshold is a pre-defined critical value for interference levels, used to determine whether environmental changes have affected the projection effect. If the threshold is exceeded, it indicates that the original projection parameters are no longer suitable for the current environment. In this case, a new projection parameter configuration scheme needs to be generated based on the new dynamic environmental data. This scheme adjusts core parameters such as brightness, contrast, and focal length according to the new projection interference level and current ground texture features. The new parameter configuration scheme is then converted into parameter adjustment commands and sent to the projection module, allowing the projection module to complete a secondary calibration.

[0067] The navigation content is updated in small increments in real time with the positioning data, while the projection parameters are only adjusted when environmental changes exceed a threshold. The two have different update frequencies and triggering conditions, which ensures the real-time nature of navigation while avoiding unnecessary frequent parameter adjustments and reducing system energy consumption.

[0068] In some specific embodiments, when calculating the scattering coefficient, air particulate matter concentration and air humidity are used as input parameters, and the wavelength-related scattering coefficient is obtained by substituting them into the Mie scattering model. When calculating the attenuation coefficient, light intensity and air particulate matter concentration are used as input parameters, and the transmission attenuation coefficient of light in the current environment is calculated based on the Lambert-Beer law. The projection interference level is determined by calculating the combined value of the scattering coefficient and the attenuation coefficient using a preset weighted formula, and then comparing it with preset low, medium, and high thresholds. By introducing classical optical models and weighted quantization algorithms, accurate and objective determination of the degree of environmental interference is achieved, providing a scientific basis for subsequent anti-interference processing. The process for determining the projection interference level is attached. Figure 4 As shown.

[0069] When calculating the scattering coefficient, air particulate matter concentration and air humidity are selected as core input parameters. This is because the main medium scattering of projected light in outdoor environments is suspended particulate matter, and air humidity directly affects the particle size, distribution, and surface characteristics of these particles, thereby altering their ability to scatter light. Selecting these two parameters comprehensively reflects the key environmental factors affecting scattering. These two parameters are then substituted into the Mie scattering model for calculation. The core advantage of this model is its applicability to scattering scenarios where the particle diameter and light wavelength are on the same order of magnitude. Common outdoor dust and water vapor particles perfectly fit this characteristic. The model calculation yields a scattering coefficient highly correlated with the wavelength of the light emitted by the projection module, rather than a generic scattering coefficient. This coefficient accurately characterizes the degree of scattering interference of the current environment on the projected light, avoiding errors caused by the mismatch between generic coefficients and actual projected light.

[0070] When calculating the attenuation coefficient, light intensity and air particulate matter concentration are selected as input parameters. Air particulate matter concentration determines the amount of light attenuation caused by absorption and scattering by particles during transmission, while light intensity reflects the strength of the outdoor background light. The stronger the background light, the more significant the effective brightness attenuation of the projected light. Combining these two parameters comprehensively covers the factors affecting light attenuation during transmission. Subsequently, calculations are performed based on the Lambert-Beer law, a classic law describing the intensity attenuation of light as it passes through an absorbing medium. The attenuation coefficient calculated using this law quantifies the degree of brightness loss of the projected light during transmission in the current environment, providing a precise numerical basis for subsequent brightness compensation.

[0071] When determining the projection interference level, a preset weighted formula is used to calculate the combined value of the scattering coefficient and the attenuation coefficient. The weighting coefficients in the formula are calibrated based on a large amount of outdoor experimental data. The core logic is to assign weights according to the degree of influence of the scattering coefficient and the attenuation coefficient on the projection effect. For example, the scattering coefficient has a more significant impact on the edge sharpness of the projected content, so it is assigned a higher weight. The combined value obtained by the weighted calculation can comprehensively reflect the overall degree of environmental interference to the projection. This combined value is then compared with preset low, medium, and high thresholds to determine the current projection interference level. The three thresholds are also determined experimentally. The low level corresponds to a scenario with slight environmental interference and the projection effect is basically unaffected; the medium level corresponds to a scenario with significant interference and the need for moderate anti-interference treatment; and the high level corresponds to a scenario with severe interference and the need for enhanced anti-interference treatment.

[0072] By combining classic models from the field of optics with the actual scenario of outdoor projection navigation, the entire process from parameter input to interference level determination is quantified, avoiding errors from subjective judgment. Its beneficial effect is that it significantly improves the accuracy and objectivity of projection interference level determination, providing a reliable basis for the precise matching of subsequent anti-interference measures. This ensures that anti-interference measures will not result in poor projection effects due to insufficient judgment, nor will they waste system resources due to excessive judgment. At the same time, the wavelength-related scattering coefficient calculation method allows interference determination to better fit the actual light characteristics of the projection module, further improving the adaptability and reliability of the entire adaptive projection navigation method.

[0073] In some specific embodiments, environmental features include light intensity, air particulate matter concentration, and air humidity; ground texture features include ground roughness, flatness, reflectivity, and texture color distribution data. The specific selection of environmental features is based on the core influencing factors of light transmission outdoors. Light intensity directly determines the contrast between the projected light and the background light, and is a key parameter affecting the recognizability of the projected content. Air particulate matter concentration is the core medium causing light scattering; the more particles, the more severe the light scattering. Air humidity changes the particle size, distribution, and surface characteristics of particles, thus indirectly affecting the degree of light scattering and attenuation. The selection of these three types of environmental features comprehensively covers the core elements affecting the transmission of projected light, avoiding redundant collection of irrelevant parameters. The selection of ground texture features revolves around the presentation effect of the projected content on the ground. Ground roughness and smoothness determine the microscopic morphology of the ground surface. Rough and uneven ground will cause diffuse reflection of projected light, while smooth and flat ground is prone to specular reflection. Both directly affect the clarity of the projected content. Reflectivity determines the intensity of the ground's reflection of projected light. Too low reflectivity will result in dim projected content, while too high reflectivity will easily produce glare. Texture color distribution data contrasts with the color of the projected content, affecting the user's efficiency in recognizing navigation information. The combination of these four types of ground texture features can fully characterize the ground's ability to present projected content, providing a precise basis for subsequent feature classification and basic projection scheme construction.

[0074] In some specific embodiments, the multimodal sensing module includes a light sensor, a particulate matter sensor, a humidity sensor, an image acquisition module, and a laser ranging module. The light sensor, particulate matter sensor, and humidity sensor are used to collect environmental features, while the image acquisition module and laser ranging module work together to collect ground texture features. The multimodal sensing module employs a collaborative architecture of dedicated sensors and composite modules, achieving accurate acquisition of different types of features.

[0075] Among them, the light sensor, particulate matter sensor, and humidity sensor are dedicated quantization sensors, respectively collecting environmental features such as light intensity, air particulate matter concentration, and air humidity. Their advantages lie in high measurement accuracy and fast response speed, enabling real-time output of quantified data to meet the accuracy requirements of subsequent calculations of scattering and attenuation coefficients. The image acquisition module and laser ranging module work collaboratively to collect ground texture features. The image acquisition module captures two-dimensional texture images of the ground, extracting texture color distribution and visual features of surface texture. The laser ranging module acquires three-dimensional height data of the ground by emitting a laser beam. By calculating the difference and distribution of height data, the roughness and smoothness of the ground are accurately quantified. Furthermore, combined with image data, the ground reflectivity can be further calculated. This solves the technical pain points of single image acquisition being unable to accurately obtain quantification parameters such as roughness and smoothness, and single laser ranging being unable to obtain visual features such as color distribution, achieving comprehensive and accurate acquisition of ground texture features.

[0076] Through the collaborative efforts of hardware modules, differentiated and precise acquisition of environmental and ground texture features is achieved, avoiding the limitations of single-hardware acquisition. Its benefits are twofold: firstly, it provides standardized, high-quality raw data for the entire adaptive projection navigation method, ensuring that subsequent steps such as scattering coefficient calculation, attenuation coefficient calculation, and ground texture classification can be based on unified and accurate data, significantly reducing data processing errors; secondly, the collaborative architecture of dedicated sensors and composite modules balances the real-time nature and accuracy of data acquisition, adapting to complex and changing outdoor environments, providing solid hardware and data support for the continuous and stable operation of the navigation system, and improving the reliability and adaptability of the entire navigation method.

[0077] In some specific embodiments, the process of obtaining the basic projection scheme includes: using a gray-level co-occurrence matrix algorithm to extract the contrast, correlation, energy, and uniformity features of the ground texture, and combining this with ground height difference data obtained by laser ranging to form a multi-dimensional ground texture feature set; outputting feature labels based on the multi-dimensional ground texture features through a preset classification model; generating a basic projection scheme adapted to the current ground texture by matching parameters in a mapping table according to the classified feature labels; and pre-setting corresponding projection brightness thresholds, contrast thresholds, focal length parameters, and initial projection angle values ​​for different feature labels in the mapping table. Through the end-to-end processing of feature extraction, feature fusion, feature classification, and parameter matching, accurate adaptation of the projection scheme to the ground texture is achieved. The overall principle is to rely on the complementary fusion of optical texture analysis algorithms and three-dimensional height data to construct a comprehensive ground feature representation system, then simplify the feature dimensions through a classification model, and finally output the optimal basic projection scheme based on a preset parameter mapping table.

[0078] In the feature extraction and fusion stage, the gray-level co-occurrence matrix algorithm is used to extract four core features of the ground texture: contrast, correlation, energy, and uniformity. The core principle of this algorithm is to quantitatively characterize the microstructural characteristics of the ground texture by statistically analyzing the spatial distribution relationship of pixel pairs of different gray levels in the ground texture image. Contrast reflects the clarity of the texture; the higher the value, the more significant the difference in brightness of the ground texture, and the greater the impact on the edge recognition of the projected content. Correlation characterizes the spatial similarity of the gray levels of texture pixels; the higher the value, the more obvious the directionality of the ground texture. For example, the correlation value of a striped texture will be higher. Energy characterizes the regularity of the texture; high energy values ​​correspond to regular and repetitive ground textures, and such textures reflect projected light more regularly. Uniformity characterizes the smoothness of the texture; the higher the value, the finer and smoother the ground texture, and vice versa.

[0079] The ground height difference data acquired by the laser ranging module can accurately quantify the three-dimensional micromorphology of the ground, directly reflecting the roughness and smoothness of the ground. However, the gray-level co-occurrence matrix algorithm can only extract the texture features of two-dimensional images and cannot capture the three-dimensional height information of the ground. By fusing the two types of data, the complementary nature of two-dimensional visual texture features and three-dimensional height morphology features is achieved. The resulting multi-dimensional ground texture feature set can comprehensively and accurately depict the physical properties of the ground, solving the technical pain points that a single two-dimensional texture feature cannot characterize the three-dimensional morphology of the ground and a single three-dimensional height data cannot reflect the visual characteristics of the texture. This lays the data foundation for subsequent accurate classification.

[0080] In the feature classification stage, the core function of the pre-set classification model is to transform the complex multi-dimensional ground texture feature set into concise feature labels. This classification model is pre-trained based on a large amount of sample data from different ground types. The training samples cover full-dimensional feature data of four ground types: strong light reflectivity, weak light absorption, rough scattering, and smooth specular surfaces. The model learns the feature distribution patterns of different ground types through pattern recognition algorithms. When a new multi-dimensional feature set is input, the model calculates the similarity between the current ground features and the features of each type of sample through feature matching, and then outputs the corresponding feature labels. The core value of this process lies in simplifying high-dimensional, complex feature data into labels that can be directly used for parameter matching, significantly reducing the complexity of subsequent parameter configuration and improving the efficiency of scheme generation. A vector machine model is selected for the classification model.

[0081] In the parameter matching stage, the mapping table is a core data carrier pre-calibrated based on numerous outdoor projection experiments. The table contains preset projection brightness thresholds, contrast thresholds, focal length parameters, and initial projection angle values ​​for each feature label, representing the optimal parameter combinations for the corresponding ground type. The calibration logic involves conducting projection experiments on different ground types using a controlled variable method, testing the projection clarity, discernibility, and user visual comfort under different parameter combinations, and ultimately selecting the optimal parameters for each ground type and embedding them into the mapping table.

[0082] For labels with strong reflectivity, the mapping table presets low brightness and high contrast thresholds to reduce glare caused by strong ground reflections and improve the edge sharpness of the projected content. For labels with rough, scattering characteristics, the mapping table presets short focal length parameters and a small spot threshold to reduce scattering interference from diffuse reflections on the projected content by minimizing the projected spot size. Once the classification model outputs feature labels, the system only needs to perform accurate matching in the mapping table based on the labels to quickly generate a basic projection scheme adapted to the current ground texture. This eliminates the need for complex real-time parameter calculations, effectively improving the response speed of scheme generation.

[0083] By fusing two-dimensional visual texture features with three-dimensional height difference features and employing a rapid parameter matching mechanism based on a pre-defined classification model and mapping table, the former addresses the technical deficiency of incomplete ground feature representation, while the latter enables efficient conversion from feature data to projection schemes. Multi-dimensional feature fusion significantly improves the accuracy of ground texture classification, avoiding classification errors caused by single features. The pre-defined mapping table enables rapid matching of projection parameters, ensuring that the basic projection scheme accurately adapts to the current ground characteristics and providing a scientific basis for subsequent parameter calibration of the projection module. The efficiency and accuracy of the entire process enable it to adapt to complex and varied outdoor ground scenarios, laying a solid foundation for improving the presentation of projection navigation content.

[0084] In some embodiments, the anti-interference processing includes the coordinated processing of backscatter correction and projection content lattice redundancy coding, and the processing intensity is linearly related to the projection interference level. Specifically, the execution logic is as follows: when the projection interference level is low, only backscatter correction is performed; when the projection interference level is medium, backscatter correction + 1st-order lattice redundancy coding is performed; when the projection interference level is high, backscatter correction + 3rd-order lattice redundancy coding is performed. By refining both the anti-interference processing strategy and the feature label-projection parameter adaptation mechanism in the adaptive projection navigation method, precise dynamic control of anti-interference processing and deep matching of projection parameters with ground texture features are achieved. This addresses the technical pain points of easy interference within projections and lack of targeted parameter adaptation in complex outdoor environments, ensuring that the navigation projection content maintains a clear and discernible presentation effect under different interference levels and ground types.

[0085] In the collaborative execution phase of anti-interference processing, this application clarifies that anti-interference processing is completed collaboratively by two core operations: backscattering correction and projection content matrix redundancy encoding. Furthermore, the processing intensity is linearly correlated with the projection interference level. The core logic of this correlation design is to adapt processing resources as needed, avoid excessive processing in low-interference scenarios that would increase system energy consumption, and ensure that the processing intensity in high-interference scenarios is sufficient to offset environmental impacts.

[0086] When the projection interference level is low, it indicates that the current environment's scattering and attenuation of projected light are relatively minor. Backscattering correction alone is sufficient to compensate for pixel shifts in the projected content caused by light scattering, eliminating the need for dot matrix redundancy coding. This simplifies the processing flow and improves response speed. When the projection interference level is medium, environmental interference has already affected the integrity of the projected content. In this case, first-order dot matrix redundancy coding is superimposed on backscattering correction. By repeatedly mirroring the core navigation dot matrix, the content's fault tolerance is improved, preventing information loss due to localized interference. When the projection interference level is high, environmental interference is severe, and conventional correction is insufficient to guarantee content recognizability. Therefore, third-order dot matrix redundancy coding is superimposed on backscattering correction. By repeating the core dot matrix three times along the horizontal, vertical, and diagonal directions, multi-layered redundancy protection is constructed. Even if some dot matrix areas are covered by interference, users can still identify complete navigation information through the remaining redundant dot matrix. This linearly correlated processing strategy achieves a precise match between anti-interference capability and the degree of environmental interference, ensuring navigation reliability in complex scenarios while maintaining system efficiency.

[0087] In some specific embodiments, the feature labels are divided into four categories: strong light reflective type, weak light absorbent type, rough scattering type, and smooth mirror type. For the strong light reflective type, the projection brightness is reduced and the contrast is increased in the projection parameters. For the weak light absorbent type, the projection brightness is increased and the projection power is increased in the projection parameters. For the rough scattering type, a short focal length projection mode is adopted and the projection spot is reduced in the projection parameters. For the smooth mirror type, the projection angle is adjusted and the anti-glare filtering mode is enabled in the projection parameters.

[0088] By categorizing different combinations of three key indicators—ground reflectivity, roughness, and flatness—four characteristic labels are formed: strong light reflectivity, weak light absorption, rough scattering, and smooth mirror surface. Each label corresponds to a ground type with similar optical characteristics, providing a clear and specific classification basis for parameter matching of the basic projection scheme.

[0089] Among them, the core characteristics of the ground surface corresponding to the high reflectivity of the strong light reflective type label are high reflectivity and high surface smoothness. These surfaces are mostly polished hard surfaces, such as marble or polished cement. Their optical characteristics show a strong directional reflection ability of projected light, which can easily cause glare on the projected content and affect user recognition. The core characteristics of the ground surface corresponding to the low reflectivity type label are low reflectivity, and the surface can be smooth or rough, such as grass, dark asphalt, or dirt surfaces. Their optical characteristics show a strong absorption ability of projected light, and the projected light is easily absorbed by the ground material, resulting in insufficient brightness and low recognizability of the projected content. The core characteristics of the ground surface corresponding to the rough scattering type label are a rough surface... High roughness, poor flatness, and medium reflectivity, such as gravel roads, cobblestone roads, and uneven dirt roads, result in significant diffuse reflection of projected light. The light scatters in all directions after being projected onto the ground, causing blurred edges and a large diffusion range for the projected content. Smooth mirror-type labels correspond to grounds with extremely high reflectivity and mirror-like surface flatness, such as smooth roads after water accumulation, icy roads, and smooth metal surfaces. Their optical characteristics include extremely strong specular reflection of projected light, with the light reflecting at a fixed angle. If the reflected light directly enters the user's line of sight, it will cause serious visual interference. At the same time, the projected content is prone to positional deviation due to the offset of the reflection angle.

[0090] The division of these four feature labels comprehensively covers common outdoor ground types and their optical characteristics. Through clear classification standards, it avoids the ambiguity and subjectivity of ground texture feature classification. Its core benefit lies in providing accurate classification basis for parameter matching of the basic projection scheme, so that the projection parameters matched for different labels can be fully adapted to the optical characteristics of the corresponding ground. From the classification level, it ensures the pertinence and effectiveness of the projection scheme, thereby improving the presentation effect of navigation projection content in different ground scenarios.

[0091] For highly reflective labels, the key characteristic of this type of ground is its high reflectivity, which easily leads to excessive reflection of projected light, causing glare and blurring of navigation content. Therefore, the corresponding projection parameter adjustment strategy is to reduce projection brightness and increase contrast. Reducing brightness suppresses glare, while increasing contrast enhances the edge outline of the navigation content, ensuring that the content is clearly legible in highly reflective environments. For low-light absorbing labels, the key characteristic of this type of ground is its low reflectivity, which easily absorbs projected light, resulting in dim content. Therefore, the adjustment strategy is to increase projection brightness and increase projection power. Increasing light intensity counteracts the light absorption effect of the ground, ensuring the visibility of the navigation content. For rough, scattering labels, the core characteristic of this type of ground is... The key characteristic of this type of surface is its unevenness, which causes severe diffuse reflection of projected light, resulting in content diffusion and reduced visibility. Therefore, the adjustment strategy is to adopt a short focal length projection mode and reduce the projection spot size. By concentrating the light energy with a short focal length and reducing the diffusion range of the spot size, the navigation content is presented in a compact and clear manner. For smooth mirror-like labels, the core characteristics of this type of surface are its flat surface and extremely high reflectivity, which easily causes specular reflection, leading to directional deviation of light and significant glare. Therefore, the adjustment strategy is to optimize the projection angle and implement anti-glare measures. By adjusting the angle, reflected light is prevented from directly entering the user's line of sight, and anti-glare measures further filter stray reflected light to ensure comfortable visibility of the navigation content.

[0092] In some specific embodiments, the backscattering correction process is as follows: using the scattering coefficient and attenuation coefficient as input parameters, the scattering offset and brightness attenuation value of the projected light in the current environment are calculated through a light transmission simulation model; the pixel coordinates of the dynamic navigation content are compensated in reverse based on the scattering offset; and the grayscale value of the navigation content is pre-enhanced based on the brightness attenuation value to complete the backscattering correction.

[0093] When performing backscattering correction, the scattering coefficient and attenuation coefficient calculated in step 102 are used as the core input parameters. These two parameters fully characterize the interference characteristics of the outdoor environment on the projected light from two dimensions: spatial offset and energy loss. The scattering coefficient directly reflects the ability of suspended particles in the environment to scatter light; the larger the coefficient, the more significant the directional offset during light transmission. The attenuation coefficient reflects the degree of energy loss caused by particle absorption and medium scattering in the transmission path; the larger the coefficient, the more severe the brightness loss when the light reaches the ground. These two parameters are then substituted into the light transmission simulation model for calculation. This model is a simulation model built on geometric optics and particle scattering theory. Its core function is to simulate the complete transmission process of projected light emitted from the wearable projection module, passing through the current outdoor environment medium, and finally being projected onto the ground. The model will calculate the scattering offset of each projected light beam due to particle scattering during transmission based on the input scattering coefficient. This offset includes two key dimensions: the direction and distance of the offset, which can accurately quantify the degree to which the light deviates from the original transmission path. At the same time, the model will calculate the brightness attenuation value generated by the light from emission to projection onto the ground based on the input attenuation coefficient. This value reflects the proportion of light energy loss and is the core basis for subsequent brightness pre-enhancement.

[0094] After obtaining the scattering offset and brightness attenuation values, the system performs two steps to correct the dynamic navigation content: The first step is pixel coordinate reverse compensation based on the scattering offset, the core logic of which is to reverse the offset. Because scattering causes the projected light to deviate from the preset transmission path, the pixels of the navigation content projected onto the ground will deviate from the target position, resulting in overall blurring and edge misalignment. Therefore, the system determines the offset direction and distance of each pixel based on the calculated scattering offset, and then performs reverse compensation on the pixel coordinates of the dynamic navigation content, that is, adjusts the coordinate position of the corresponding pixel in the opposite direction of the scattering offset, thereby offsetting the scattering offset during the light transmission process, ensuring that the corrected navigation content can accurately fall on the preset target area when projected onto the ground, solving the problems of projected content position misalignment and edge blurring; The second step is grayscale pre-enhancement based on the brightness attenuation value, the core logic of which is to compensate for energy loss in advance. Light loses some energy during transmission due to attenuation. Direct projection of light onto the ground results in insufficient brightness and low visibility of navigation content. Therefore, the system pre-enhances the grayscale value of dynamic navigation content according to the calculated brightness attenuation value. For example, when the brightness attenuation value is 30%, the system will pre-enhance the grayscale value of the navigation content by 30% to offset the energy loss during light transmission. This ensures that the navigation content projected onto the ground maintains appropriate brightness to meet the user's recognition needs. Through these two coordinated steps, backscattering correction of dynamic navigation content is achieved.

[0095] In some specific embodiments, the specific implementation process of projection content dot matrix redundancy encoding is as follows: the core information of dynamic navigation content is converted into a standardized dot matrix, and repeated arrangement is performed around the core dot matrix according to the redundancy order; wherein the first-order redundancy encoding is a single mirror repetition of the core dot matrix, and the third-order redundancy encoding is a three-time staggered repetition of the core dot matrix along the horizontal, vertical and diagonal directions, and the encoded data has a built-in CRC cyclic redundancy check code for data integrity verification after projection.

[0096] When performing dot matrix redundancy encoding of projected content, the core information of the dynamic navigation content must first be converted into a standardized dot matrix. The core logic of this step is to extract the key elements in the navigation content that play a crucial role in the user's navigation decisions, such as turn arrows, distance markers, and path direction indicators, and remove redundant decorative pixel information. Then, these core elements are converted into a standardized binary dot matrix with uniform resolution and pixel size. The purpose of standardization is to eliminate the impact of differences in navigation content formats on redundant arrangement, ensure the accuracy and consistency of subsequent repetitive operations, and significantly reduce the complexity of data processing, thereby improving encoding efficiency.

[0097] Subsequently, the redundancy order is repeatedly arranged around the core dot matrix according to the preset redundancy order. The selection of the redundancy order is directly linked to the projection interference level, adapting to environmental interference of different intensities: the first-order redundancy code is a single mirror repetition of the core dot matrix. Specifically, the horizontal or vertical mirror direction can be selected according to the common outdoor interference direction (such as the lateral scattering of particles caused by crosswinds). The core dot matrix is ​​mirrored and arranged on one or both sides of the core area along the selected direction, forming a combination structure of "core dot matrix + mirror dot matrix". This arrangement method can ensure the recognizability of core information through the complementary effect of the mirror dot matrix when the local dot matrix is ​​covered by interference, and is suitable for medium interference level scenarios; 3 The redundancy coding involves three staggered repetitions of the core dot matrix along the horizontal, vertical, and diagonal directions. In practice, the core dot matrix is ​​first translated and repeated along the horizontal direction, then superimposed and repeated along the vertical direction, and finally the repeated dot matrix is ​​staggered along the two diagonal directions to form a multi-layered staggered redundancy protection structure. The staggered arrangement design can avoid the overall failure of the dot matrix caused by interference in a single direction. Even if the core dot matrix and the redundant dot matrix in a certain direction are severely interfered with, the redundant dot matrix in the remaining directions can still completely carry the core navigation information, which is suitable for high interference level scenarios.

[0098] After completing the redundancy arrangement of different orders, the system will embed a CRC (Cyclic Redundancy Check) code in the encoded data. This check code is a feature value calculated based on the encoded dot matrix data using a preset CRC algorithm. Its core function is to verify the integrity of the projected data. When the projection module projects the encoded navigation content onto the ground, the system can collect the ground projection image and extract the CRC check code from it, and compare it with the check code of the original encoded data. If the two match, the projected data is determined to be complete and valid. If they do not match, the data is determined to be lost or distorted, which can trigger a mechanism to adjust the projection parameters or reproject the content, ensuring that the navigation information obtained by the user is complete and accurate.

[0099] The hierarchical redundancy arrangement strategy achieves precise matching between anti-interference strength and environmental interference level, avoiding the waste of system resources caused by over-encoding in low-interference scenarios. Meanwhile, the CRC check mechanism compensates for the technical shortcomings of traditional redundancy encoding, which only emphasizes fault tolerance and neglects data integrity. Its beneficial effects are that, through standardized dot matrix transformation and hierarchical redundancy arrangement, it significantly improves the anti-loss and anti-distortion capabilities of core navigation information in complex interference environments. Even if some dot matrix areas are scattered or obscured by the environment, users can still identify complete navigation information through the redundant dot matrix. The built-in CRC cyclic redundancy check code achieves closed-loop verification of data integrity after projection, ensuring that the navigation content projected onto the ground is consistent with the original encoded content, avoiding navigation information errors caused by data transmission or projection errors. Furthermore, this encoding method synergizes with backscattering correction; the former solves the problem of position and brightness offset of navigation content, while the latter solves the problem of fault tolerance and integrity of navigation content. The combination of the two significantly improves the anti-interference performance of the entire adaptive projection navigation method, ensuring that navigation information can still be presented clearly, accurately, and completely in harsh outdoor environments with high scattering and high attenuation.

[0100] An adaptive projection navigation system based on outdoor environment, the system modules are shown in the attached figure. Figure 5 As shown, the adaptive projection navigation system includes:

[0101] Data acquisition unit 1 is used to acquire environmental features and ground texture features of the outdoor environment through a multimodal perception module;

[0102] Scheme construction unit 2 is used to calculate the scattering coefficient and attenuation coefficient of light based on environmental characteristics, and then determine the projection interference level of the outdoor environment; feature extraction and classification of ground texture features are performed to obtain feature labels, and a basic projection scheme for the outdoor environment is constructed around the feature labels;

[0103] Projection initialization unit 3 is used to convert the basic projection scheme into parameter configuration instructions and send them to the projection module worn by the user to complete the initial parameter calibration of the projection module.

[0104] Projection correction unit 4 is used to generate dynamic navigation content based on navigation path planning results and real-time positioning data, and to perform anti-interference processing on the dynamic navigation content in combination with the projection interference level to obtain navigation projection content; the anti-interference processing includes backscattering correction and projection content dot matrix redundancy encoding;

[0105] Projection output unit 5 is used to convert navigation projection content into content display instructions and send them to the projection module. The projection module then projects the navigation information onto the ground in front of the user based on the calibrated projection parameters.

[0106] Those skilled in the art will understand that the modules described above can be implemented using general-purpose computing systems. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Optionally, they can be implemented using computer-executable program code, allowing them to be stored in a storage system for execution by the computing system. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0107] Note that the above description is merely a preferred embodiment and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of this application, and the scope of this application is determined by the scope of the appended claims.

[0108] The above disclosures are only a few specific implementation scenarios of this application. However, this application is not limited to these. Any variations that can be conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. An adaptive projection navigation method based on outdoor environment, characterized in that, include: The multimodal perception module collects environmental features and ground texture features of the outdoor environment; The scattering coefficient and attenuation coefficient of light are calculated based on the environmental characteristics to determine the projection interference level of the outdoor environment; feature extraction and classification are performed on the ground texture features to obtain feature labels, and a basic projection scheme for the outdoor environment is constructed based on the feature labels. The basic projection scheme is converted into parameter configuration instructions and sent to the projection module worn by the user to complete the initial parameter calibration of the projection module. Dynamic navigation content is generated based on navigation path planning results and real-time positioning data. Anti-interference processing is then performed on the dynamic navigation content in conjunction with the projection interference level to obtain navigation projection content. The anti-interference processing includes backscattering correction and projection content dot matrix redundancy encoding. The navigation projection content is converted into content display instructions and sent to the projection module. The projection module then projects the navigation information onto the ground in front of the user based on the calibrated projection parameters. The process of obtaining the basic projection scheme includes: The gray-level co-occurrence matrix algorithm is used to extract the contrast, correlation, energy and uniformity features of the ground texture. At the same time, the ground height difference data obtained by laser ranging is combined to form a multi-dimensional ground texture feature set. Feature labels are obtained by outputting the multi-dimensional ground texture feature set through a preset classification model; Based on the parameters in the matching mapping table obtained from the classification feature labels, a basic projection scheme adapted to the current ground texture is generated; the mapping table presets corresponding projection brightness threshold, contrast threshold, focal length parameter and initial value of projection angle for different feature labels.

2. The adaptive projection navigation method according to claim 1, characterized in that, Also includes: Real-time acquisition of positioning data and dynamic environmental data during navigation; updating dynamic navigation content based on the positioning data and issuing content display instructions. If changes in dynamic environmental data cause the projection interference level to exceed the preset threshold, the projection parameter configuration scheme will be regenerated and parameter adjustment instructions will be issued to achieve differentiated dynamic updates of projection parameters and navigation content, thus completing adaptive navigation projection.

3. The adaptive projection navigation method according to claim 1, characterized in that, When calculating the scattering coefficient, air particulate matter concentration and air humidity are used as input parameters and substituted into the Mie scattering model to obtain the wavelength-related scattering coefficient. When calculating the attenuation coefficient, light intensity and air particulate matter concentration are used as input parameters, and the transmission attenuation coefficient of light in the current environment is calculated based on the Lambert-Beer law. The projection interference level is determined by calculating the combined value of the scattering coefficient and the attenuation coefficient using a preset weighted formula, and then comparing it with preset low, medium and high threshold values.

4. The adaptive projection navigation method according to claim 1, characterized in that, The environmental features include light intensity, air particulate matter concentration, and air humidity; the ground texture features include ground roughness, flatness, reflectivity, and texture color distribution data.

5. The adaptive projection navigation method according to claim 1, characterized in that, The multimodal sensing module includes a light sensor, a particulate matter sensor, a humidity sensor, an image acquisition module, and a laser ranging module; wherein, the light sensor, particulate matter sensor, and humidity sensor are used to collect environmental features, and the image acquisition module and the laser ranging module work together to collect ground texture features.

6. The adaptive projection navigation method according to claim 1, characterized in that, The feature labels are divided into four categories: strong light reflective type, weak light absorbent type, rough scattering type, and smooth mirror type. For projects with strong glare, reduce the projection brightness and increase the contrast in the projection parameters. For low-light absorption types, increase the projection brightness and projection power in the projection parameters; For coarse scattering type, a short focal length projection mode is adopted in the projection parameters and the projection spot size is reduced; For smooth mirror surfaces, adjust the projection angle in the projection parameters and enable the anti-glare filter mode.

7. The adaptive projection navigation method according to claim 1, characterized in that, The specific implementation process of the backscattering correction is as follows: Using the scattering coefficient and attenuation coefficient as input parameters, the scattering offset and brightness attenuation value of the projected light in the current environment are calculated through a light transmission simulation model. Based on the scattering offset, the pixel coordinates of the dynamic navigation content are compensated in reverse, and based on the brightness attenuation value, the grayscale value of the navigation content is pre-enhanced to complete the backscattering correction.

8. The adaptive projection navigation method according to claim 1, characterized in that, The specific implementation process of the projection content dot matrix redundancy encoding is as follows: the core information of the dynamic navigation content is converted into a standardized dot matrix, and repeated arrangement is performed around the core dot matrix according to the redundancy order; wherein the first-order redundancy encoding is a single mirror repetition of the core dot matrix, and the third-order redundancy encoding is a three-fold staggered repetition of the core dot matrix along the horizontal, vertical and diagonal directions, and the encoded data has a built-in CRC cyclic redundancy check code for data integrity verification after projection.

9. An adaptive projection navigation system based on outdoor environment, characterized in that, include: The data acquisition unit is used to collect environmental features and ground texture features of the outdoor environment through the multimodal perception module; The scheme construction unit is used to calculate the scattering coefficient and attenuation coefficient of light based on the environmental characteristics, and then determine the projection interference level of the outdoor environment; to extract and classify the ground texture features to obtain feature labels, and to construct a basic projection scheme for the outdoor environment based on the feature labels; The projection initialization unit is used to convert the basic projection scheme into parameter configuration instructions and send them to the projection module worn by the user to complete the initial parameter calibration of the projection module. The projection correction unit is used to generate dynamic navigation content based on navigation path planning results and real-time positioning data, and to perform anti-interference processing on the dynamic navigation content in combination with the projection interference level to obtain navigation projection content; the anti-interference processing includes backscattering correction and projection content dot matrix redundancy encoding. The projection output unit is used to convert the navigation projection content into content display instructions and send them to the projection module. The projection module then projects the navigation information onto the ground in front of the user based on the calibrated projection parameters. The process of obtaining the basic projection scheme includes: The gray-level co-occurrence matrix algorithm is used to extract the contrast, correlation, energy and uniformity features of the ground texture. At the same time, the ground height difference data obtained by laser ranging is combined to form a multi-dimensional ground texture feature set. Feature labels are obtained by outputting the multi-dimensional ground texture feature set through a preset classification model; Based on the parameters in the matching mapping table obtained from the classification feature labels, a basic projection scheme adapted to the current ground texture is generated; the mapping table presets corresponding projection brightness threshold, contrast threshold, focal length parameter and initial value of projection angle for different feature labels.

Images