Autonomous navigation method and device based on multi-source perception information fusion, equipment and medium

Abstract

The present application belongs to the field of autonomous navigation technology, and provides a kind of multi-source perception information fusion autonomous navigation method, device, equipment and medium, solve the navigation positioning problem under the environment of no signal direct path, comprising: obtaining Wi-Fi channel state information, initial estimated position, access point position and environmental reflection surface data, predict theoretical reflection path and the predicted time delay and reflection point position of each theoretical reflection path, extract the observation time delay of each observed multipath component, calculate the time delay deviation value of each observation time delay and each predicted time delay, to build the corresponding observation multipath component and theoretical reflection path matching association accordingly, when there are at least three theoretical reflection paths that exist matching association, calculate virtual ranging value, construct positioning equation set according to virtual ranging value and solve, obtain positioning coordinate, to generate navigation control instruction accordingly.The present application can realize accurate positioning navigation by constructing virtual signal source through multipath reflection path under the environment of no signal direct path.

Description

Technical Field

[0001] This invention relates to the field of autonomous navigation technology, specifically to an autonomous navigation method, apparatus, device, and medium that integrates multi-source sensing information. Background Technology

[0002] With the widespread application of intelligent mobile devices in indoor environments, accurate and continuous positioning capabilities have become one of the core challenges in achieving efficient autonomous navigation. Due to factors such as building structure obstruction, GPS signals are severely attenuated or even unusable in indoor environments, making Wi-Fi-based positioning technology widely studied due to its advantages such as widespread infrastructure and low deployment costs. However, complex indoor environments generally exhibit significant multipath propagation effects, where wireless signals are reflected by objects such as walls and shelves, forming multiple propagation paths, causing time delay spread and signal interference, making it difficult to accurately extract the direct path.

[0003] Current mainstream positioning technologies typically treat multipath signals as interference factors, employing techniques such as super-resolution algorithms, matched filtering, and beamforming to suppress or eliminate multipath components. They also utilize statistical modeling and robust estimation to mitigate errors introduced by non-line-of-sight propagation. These methods effectively improve positioning accuracy in scenarios with stable direct signal paths. However, in highly structured and heavily obstructed environments such as warehouses, factories, and underground parking lots, the direct signal path between the device and the access point (AP) is chronically blocked due to obstacles like metal shelves and concrete walls, significantly degrading or even rendering traditional positioning models ineffective.

[0004] It is worth noting that regular reflective surfaces (such as shelf surfaces and walls) that are widely present in highly structured and heavily obstructed environments such as warehouses, factories, and underground parking lots, while blocking direct paths, provide the geometric conditions for forming stable reflection paths. Meanwhile, intelligent mobile vehicles (various unmanned intelligent equipment) are typically equipped with lidar or visual sensors, which can acquire the spatial position and attitude information of these reflective surfaces in real time. Since multipath propagation characteristics are closely related to environmental geometry, combining environmental maps with Wi-Fi channel state information can predict multipath propagation paths and infer their reflection point locations, thus treating the reflection paths as "virtual signal sources" for localization. However, existing multipath suppression or multi-sensor fusion methods do not fully utilize the geometric constraint information in these reflection paths, neglecting in-depth mining of the geometric constraint information embedded in multipath signals, and do not systematically incorporate reflection paths into the localization observation model.

[0005] In existing indoor positioning technologies, methods relying on direct signal paths face challenges in terms of positioning accuracy and continuity when these paths are consistently blocked. Obstacles such as metal shelves and concrete walls in complex indoor environments obstruct wireless signals, making it difficult to establish a stable line-of-sight transmission link between the device and the access point. This increases the difficulty for traditional positioning models based on signal arrival time or signal strength to obtain effective ranging information. While reflected signals from multipath propagation possess potential geometric constraint information, current technologies treat them as interference sources and suppress them, failing to fully utilize the virtual signal source characteristics implicit in the reflected path. This results in the positioning system being unable to establish sufficient spatial constraint equations in scenarios without a direct signal path, leading to decreased positioning accuracy. For example, in automated warehousing systems, unmanned transport vehicles (ARTVs) need to perform goods transfer tasks within aisles densely packed with metal shelves. Limited by shelf height and spacing, the ARTVs and Wi-Fi access points deployed on the ceiling are in a non-line-of-sight state for extended periods. In this situation, traditional positioning algorithms, unable to identify a direct path, continuously output abnormal position estimates, causing the ARTVs to deviate from their intended path.

[0006] If the above problems are not addressed, the reliability of the positioning system will continue to deteriorate as the degree of environmental obstruction increases, making it difficult for mobile vehicles to maintain stable and continuous navigation capabilities in complex indoor scenarios. Therefore, it is urgent to study a new positioning mechanism that can still operate robustly under conditions without a direct path, and to fully explore and utilize the geometric information contained in multipaths to improve the adaptability and robustness of the system's positioning in obstructed environments. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides an autonomous navigation method, apparatus, device, and medium based on multi-source sensing information fusion. This method transforms multipath interference factors into usable resources, enabling autonomous navigation through multi-source sensing information fusion. Furthermore, this invention can achieve accurate positioning and navigation in environments without direct signal paths by constructing virtual signal sources through multipath reflection paths.

[0008] To achieve the above objectives, the present invention provides the following technical solution: On the one hand, an autonomous navigation method based on multi-source sensing information fusion is provided, comprising the following steps: Acquire the current Wi-Fi channel status information, initial estimated location, Wi-Fi wireless access point location, and environmental reflective surface data of the mobile carrier; Based on environmental reflective surface data and the location of Wi-Fi access points, predict at least one theoretical reflection path from the location of the Wi-Fi access point to the initial estimated location, as well as the predicted delay and reflection point location for each theoretical reflection path. The observation delay of each observation multipath component of the mobile carrier is extracted based on the Wi-Fi channel state information; Calculate the time delay deviation between each observation time delay and each prediction time delay, and obtain the observed multipath components and theoretical reflection paths whose time delay deviation values ​​are less than the preset deviation threshold; Construct a matching correlation between observed multipath components and theoretical reflection paths; The number of theoretical reflection paths with matching associations is counted. When the number of theoretical reflection paths with matching associations is greater than a set threshold, the reflection point location of the theoretical reflection path with matching associations is taken as a virtual access point. The virtual ranging value from the mobile vehicle to the virtual access point is calculated based on the observation delay of the corresponding observation multipath component with matching associations. Based on the location of each virtual access point and its corresponding virtual ranging value, a set of positioning equations is constructed; Solve the positioning equations to obtain the positioning coordinates of the mobile vehicle; Based on the positioning coordinates of the mobile carrier, navigation control commands are generated to drive the mobile carrier to the target position.

[0009] On the other hand, an autonomous navigation device that integrates multi-source sensing information is provided, comprising: The data acquisition module is used to acquire the current Wi-Fi channel status information, initial estimated location, Wi-Fi wireless access point location, and environmental reflective surface data of the mobile carrier; The reflection prediction module is used to predict at least one theoretical reflection path from the Wi-Fi wireless access point location to the initial estimated location based on environmental reflective surface data and the location of the Wi-Fi wireless access point, as well as the predicted time delay and reflection point location of each theoretical reflection path; The delay extraction module is used to extract the observation delay of each observation multipath component of the mobile carrier based on the Wi-Fi channel state information; The time delay deviation calculation module is used to calculate the time delay deviation value between each observation time delay and each prediction time delay, and to obtain the observed multipath components and theoretical reflection paths whose time delay deviation values ​​are less than the preset deviation threshold. The matching and association module is used to construct the matching and association between observed multipath components and theoretical reflection paths; The virtual ranging module is used to count the number of theoretical reflection paths with matching associations. When the number of theoretical reflection paths with matching associations is greater than a set threshold, the reflection point position of the theoretical reflection path with matching associations is used as a virtual access point. The virtual ranging value from the mobile vehicle to the virtual access point is calculated based on the observation delay of the corresponding observation multipath component with matching associations. The equation construction module is used to construct a set of positioning equations based on the location of each virtual access point and its corresponding virtual ranging value. The equation solving module is used to solve the positioning equation set to obtain the positioning coordinates of the mobile carrier; A designated generation module is used to generate navigation control commands that drive the mobile carrier to the target position based on the positioning coordinates of the mobile carrier.

[0010] On the other hand, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-mentioned autonomous navigation method of multi-source sensing information fusion.

[0011] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described autonomous navigation method for multi-source sensing information fusion.

[0012] On the other hand, the present invention provides a computer program product stored on a computer-readable storage medium and including computer instructions that, when executed by a processor, cause an electronic device to implement the steps of the above-described autonomous navigation method for multi-source sensing information fusion.

[0013] Compared with existing technologies, the autonomous navigation method based on multi-source sensing information fusion provided by this invention has the following advantages: This invention can construct a virtual signal source by fusing environmental reflective surface data with the observed multipath component characteristics of Wi-Fi signals to achieve autonomous positioning of mobile vehicles on paths without direct signal access. It has the advantage of improving navigation accuracy by utilizing geometric constraints of reflection paths in complex obstructed environments. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of an autonomous navigation method that integrates multi-source sensing information in one embodiment; Figure 2 This is a block diagram of an autonomous navigation device that fuses multi-source sensing information in one embodiment; Figure 3 This is a block diagram of an electronic device in one embodiment. Detailed Implementation

[0016] The technical solution of the present invention will now be clearly and completely described through specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0017] Existing technologies eliminate interference by suppressing multipath components, but this strategy leads to insufficient positioning information in scenarios without direct paths. To address this, this invention first considers how to transform multipath signals from interference factors into positioning resources. Since the geometric structure information of environmental reflective surfaces is strongly correlated with multipath propagation paths, combining reflective surface data acquired by lidar or visual sensors with Wi-Fi channel state information can predict signal reflection paths and establish virtual ranging constraints. Furthermore, by matching the time delay characteristics of observed multipath components and predicted reflection paths, effective reflection paths that meet geometric constraints are selected, and their reflection points are transformed into virtual access points, thereby constructing a sufficient set of positioning equations to solve for the location coordinates.

[0018] Reference Figure 1 In one embodiment, an autonomous navigation method based on multi-source sensing information fusion is provided, which can be applied to the positioning and navigation of mobile carriers on routes without direct signal coverage. The method includes the following steps: Acquire the current Wi-Fi channel status information, initial estimated location, Wi-Fi wireless access point location, and environmental reflective surface data of the mobile carrier; Based on environmental reflective surface data and the location of Wi-Fi access points, predict at least one theoretical reflection path from the location of the Wi-Fi access point to the initial estimated location, as well as the predicted delay and reflection point location for each theoretical reflection path. The observation delay of each observation multipath component of the mobile carrier is extracted based on the Wi-Fi channel state information; Calculate the time delay deviation between each observation time delay and each prediction time delay, and obtain the observed multipath components and theoretical reflection paths whose time delay deviation values ​​are less than the preset deviation threshold; Construct a matching correlation between observed multipath components and theoretical reflection paths; The number of theoretical reflection paths with matching associations is counted. When the number of theoretical reflection paths with matching associations is greater than a set threshold, the reflection point location of the theoretical reflection path with matching associations is taken as a virtual access point. The virtual ranging value from the mobile vehicle to the virtual access point is calculated based on the observation delay of the corresponding observation multipath component with matching associations. Based on the location of each virtual access point and its corresponding virtual ranging value, a set of positioning equations is constructed; Solve the positioning equations to obtain the positioning coordinates of the mobile vehicle; Based on the positioning coordinates of the mobile carrier, navigation control commands are generated to drive the mobile carrier to the target position.

[0019] The mobile carrier can be various unmanned mobile equipment, mobile robots, etc.

[0020] The Wi-Fi channel state information mentioned in this invention refers to the multipath component parameters, including signal amplitude, phase, and delay, obtained through a wireless communication protocol. Specifically, it can be extracted using the channel frequency response matrix in orthogonal frequency division multiplexing technology to characterize the multipath effect during signal propagation.

[0021] The initial estimated position refers to a rough initial position value used by the mobile vehicle when it starts the current positioning calculation. It is used as the geometric starting point for predicting the reflection path. Specifically, it can be obtained based on the dead reckoning results of the mobile vehicle's inertial measurement unit, wheel speedometer, or visual sensor. In some implementations, if the mobile vehicle has just started and has no prior position information, a coarse-grained region center point can be used as the initial estimate, or the initial estimated position can be obtained by coarse positioning through a Wi-Fi fingerprint database.

[0022] The location of a Wi-Fi access point refers to its three-dimensional coordinates in the environment, which can be obtained through measurement and calibration during the deployment phase.

[0023] Environmental reflective surface data refers to the geometric information of the reflective surfaces of objects in the environment that can reflect wireless signals. Specifically, it can be obtained by collecting point cloud data through lidar or depth cameras and fitting plane equations, which is used to construct geometric constraints for signal reflection paths.

[0024] In the above embodiments, based on environmental reflector data and the location of the Wi-Fi access point, at least one theoretical reflection path from the Wi-Fi access point location to the initially estimated location is predicted, along with the predicted time delay and reflection point location for each theoretical reflection path. In scenarios without a direct signal path, there is no line-of-sight signal between the mobile vehicle and the Wi-Fi access point location that can be directly used for ranging. Traditional positioning methods based on time of arrival or angle of arrival cannot establish effective geometric constraints. By predicting possible signal reflection paths based on environmental reflector data and the location of the Wi-Fi access point, the propagation trajectory and propagation delay of the signal from the access point through the reflector to the mobile vehicle can be estimated under conditions without a direct link. This allows the reflection path to be equivalent to an effective ranging channel emitted by a virtual signal source, providing a theoretical reference for subsequent time delay matching and location determination.

[0025] In one embodiment, a method is proposed for predicting at least one theoretical reflection path from the location of a Wi-Fi wireless access point to an initially estimated location, and for predicting the prediction delay and reflection point location of each theoretical reflection path, including the following steps: The reflective surface position and normal vector of the target reflective surface are extracted based on the environmental reflective surface data. The target reflective surface is a reflective surface that may reflect signals originating from the Wi-Fi wireless access point to the initial estimated position. Based on the position of the reflective surface and the normal vector, the mirror image position of the Wi-Fi wireless access point with respect to the target reflective surface is calculated using the mirror method. Based on the mirror point position and the initial estimated position, the position of the reflection point of the signal reflected from the target reflective surface to the moving carrier is calculated by geometric intersection; The theoretical reflection path is the path that starts from the location of the Wi-Fi wireless access point, passes through the location of the reflection point, and reflects back to the initial estimated location. Based on the location of the Wi-Fi wireless access point and the location of the reflection point, calculate the reflection path length of each theoretical reflection path to the initial estimated location; The predicted time delay of each theoretical reflection path is calculated based on the reflection path length and the signal propagation speed.

[0026] In the above embodiments, the target reflective surface can be determined based on geometric reachability constraints. That is, for each environmental reflective surface, the incident angle and reflection angle of the Wi-Fi wireless access point position and the initial estimated position relative to the environmental reflective surface are calculated, and it is determined whether the reflection law is satisfied. If the incident point and the reflection point are both located within the effective boundary range of the reflective surface space, and the reflection path is not blocked by other obstacles, then the environmental reflective surface is determined as the target reflective surface.

[0027] Optionally, threshold conditions can be set based on parameters such as the angle between the normal vector of the reflecting surface and the line connecting the access point to the reflecting surface, the length of the reflection path, and the material reflection coefficient, in order to exclude environmental reflecting surfaces with excessive reflection loss or infeasible paths.

[0028] In the above embodiments, based on the position of the reflecting surface and the normal vector, the position of the Wi-Fi wireless access point relative to the target reflecting surface is calculated using the mirror method. Specifically, the mirror method is a classic method in computational geometry for handling specular reflection problems, given the position of the Wi-Fi wireless access point... and the equation of the reflecting surface Then the position of the mirror point The calculation formula is: ; in, unit normal vector The equation for the reflector surface is obtained by fitting point cloud data.

[0029] For example, for those located in The access point and normal vector are plane equation The position of the mirror point is calculated as follows: ; In the above embodiments, based on the mirror point position and the initial estimated position, the signal is geometrically intersected to calculate the reflection point position of the moving carrier reflected on the target reflective surface. Specifically, the reflection point is the intersection of the straight line segment connecting the mirror point and the initial estimated position with the reflective surface. Assuming the initial estimated position is... Then the parametric equation of the line connecting the mirror point and the initial estimated position is: ; Substitute the parametric equation into the reflector equation and solve for the parameters. : ; Reflection point location That is The value; It should be noted that it is necessary to verify whether the position of the reflection point is within the effective boundary of the reflecting surface. If the reflecting surface is a finite plane, it is necessary to check whether the coordinates of the reflection point are within the polygonal boundary of the plane. If it exceeds the boundary, the position of the reflection point is invalid.

[0030] Continuing with the previous example, assuming the initial estimated position is (8, 15, 0.5), the equation of the line is: ; Substitute into the plane equation ,have to: ; Substituting t into the equation of the line, we can obtain the position of the reflection point. for: ; Right now (12.5, 13.125, 1.25).

[0031] In the above embodiments, the path from the Wi-Fi wireless access point location, through the reflection point location, to the initial estimated location is taken as the theoretical reflection path. The theoretical reflection path consists of two straight lines: the incident segment from the Wi-Fi wireless access point to the reflection point and the reflection segment from the reflection point to the initial estimated location. This path physically corresponds to a possible propagation trajectory of a signal emitted from the Wi-Fi wireless access point, reflected by the reflective surface, and reaching the mobile carrier. For example, in the example above, the theoretical reflection path is: →(12.5, 13.125, 1.25)→ .

[0032] In the above embodiments, based on the location of the Wi-Fi wireless access point and the location of the reflection point, the reflection path length of each theoretical reflection path to the initial estimated location is calculated, wherein the reflection path length... It is the sum of the incident section length and the reflection section length: ; in, This represents the Euclid distance. Indicates the location of the reflection point. Indicates the location of the access point. This indicates the initial estimated location.

[0033] Continuing with the previous example, the theoretical reflection path can be obtained from the above formula. →(12.5, 13.125, 1.25)→ The reflection path length is approximately 13.14 meters, while the Wi-Fi wireless access point To the initial estimated position The straight-line distance is approximately 10.3 meters, and the length of the reflection path is significantly greater than this straight-line distance, which is consistent with the physical characteristics of the reflection path.

[0034] In the above embodiments, the predicted time delay of each theoretical reflection path is calculated based on the reflection path length and the signal propagation speed. The signal propagation speed is the speed of electromagnetic wave propagation in air, which is approximately the speed of light. m / s, the quotient of the reflection path length and c is the prediction delay; In the aforementioned example, predicting latency The predicted latency will be matched with the observed latency extracted from the Wi-Fi channel state information.

[0035] It should be noted that if there are multiple target reflective surfaces in the environment, the above steps need to be performed on each reflective surface separately to obtain multiple theoretical reflection paths and their corresponding predicted delays. In more complex cases, secondary reflection paths also need to be considered, i.e., the signal arrives after being reflected by two reflective surfaces. In this case, it is necessary to traverse the combination of reflective surfaces and calculate more complex image chains.

[0036] By combining environmental geometric information with electromagnetic wave propagation theory, the above scheme enables the prediction of signal propagation trajectories under conditions of no direct path. This prediction does not depend on actual signal observations but is based on prior knowledge of the environmental map for reasoning, providing a theoretical reference system for subsequent observation data matching.

[0037] The autonomous navigation method based on multi-source sensing information fusion provided by this invention includes extracting the observation delay of each observed multipath component of the mobile vehicle based on Wi-Fi channel state information. This delay is the propagation time delay experienced by each observed multipath component from the Wi-Fi wireless access point to the mobile vehicle. Specifically, this can be achieved by performing an inverse Fourier transform on the channel frequency response to obtain the channel impulse response, and then detecting the peak position using a peak detection algorithm. It should be noted that the accuracy of the observation delay extraction is limited by the bandwidth of the Wi-Fi system. For a 20MHz bandwidth 802.11n system, the theoretical delay resolution is approximately 50ns, while for an 80MHz bandwidth 802.11ac system, the theoretical delay resolution can be improved to 12.5ns. Higher bandwidth allows for better separation of adjacent multipaths, improving the accuracy of subsequent matching.

[0038] After obtaining the predicted delay of each theoretical reflection path and the observation delay of each observed multipath component, the delay deviation between each observation delay and each predicted delay is calculated. Based on the delay deviation, it is determined whether the observed multipath component and the theoretical reflection path meet the delay matching condition. For the observed multipath component and the theoretical reflection path that meet the delay matching condition, a matching association between the observed multipath component and the theoretical reflection path is constructed. Specifically, the delay matching condition is: if the delay deviation between the observation delay of the observed multipath component and the predicted delay of the theoretical reflection path is less than a preset deviation threshold, then the corresponding observed multipath component and the theoretical reflection path meet the delay matching condition. Delay matching is the core step of multipath-based positioning. Its purpose is to establish a correct correspondence between the observed multipath signal and the predicted reflection path. Only accurately matched multipath-path pairs can be used for subsequent positioning calculations.

[0039] Continuing with the warehouse scenario example, suppose seven theoretical reflection paths were predicted: path A - 14.8ns, path B - 29.2ns, path C - 43.8ns, path D - 52.1ns, path E - 67.5ns, path F - 81.3ns, and path G - 95.7ns. In the previous steps, the observation delays of five observed multipath components were extracted: multipath 1 - 15.2ns, multipath 2 - 28.7ns, multipath 3 - 43.1ns, multipath 4 - 56.3ns, and multipath 5 - 68.9ns. The preset deviation threshold is 5ns. Based on this threshold, the matching pairs with a deviation threshold less than 5ns are multipath 1. Path A (deviation 0.4ns), multipath 2 Path B (deviation 0.5ns), multipath 3 Path C (deviation 0.7ns), multipath 4 Path D (deviation 4.2ns), multipath 5 Path E (deviation 1.4ns) and these 5 matching associations will be retained, while paths F and G will not participate in subsequent positioning calculations because no multipath observations have been found to match them.

[0040] It should be noted that in practical applications, there may be one-to-many or many-to-one matching situations. In this case, an optimal allocation algorithm (such as the Hungarian algorithm) is required to perform global optimal matching to ensure that: each observed multipath component is matched with at most one theoretical reflection path, and each theoretical reflection path is matched with at most one observed multipath component.

[0041] To further improve the reliability of the matching, in a preferred embodiment, under the premise of time delay matching, an amplitude consistency verification mechanism is also introduced. That is, after obtaining multiple sets of observed multipath components and theoretical reflection paths that satisfy the time delay matching condition through time delay matching, the amplitude consistency verification mechanism is further executed. Matching associations are only constructed for observed multipath components and theoretical reflection paths that simultaneously satisfy both time delay matching and amplitude consistency verification. Specifically, the amplitude consistency verification mechanism includes the following steps: Obtain the observed multipath components and theoretical reflection paths when the time delay deviation is less than a preset deviation threshold; The observation amplitude of the observed multipath component is extracted based on the Wi-Fi channel state information; The reflection coefficient of the target reflecting surface corresponding to the theoretical reflection path is extracted based on the environmental reflecting surface data; The predicted amplitude of the theoretical reflection path is calculated based on the reflection coefficient and the reflection path length; Calculate the ratio of the observed amplitude of the observed multipath component to the predicted amplitude of the theoretical reflection path; Determine whether the ratio is within a preset ratio range. If so, construct a matching association between the observed multipath component and the theoretical reflection path.

[0042] In the above embodiments, the observed amplitude of the observed multipath component is extracted based on the Wi-Fi channel state information. Specifically, the observed amplitude can be obtained by performing an inverse Fourier transform on the channel frequency response to obtain the channel impulse response, and then detecting the peak amplitude through a peak detection algorithm. Alternatively, it can be extracted by amplitude spectrum analysis of the frequency domain channel response.

[0043] In the above embodiments, the reflection coefficient of the reflection surface corresponding to the theoretical reflection path is extracted based on the environmental reflection surface data. The reflection coefficient can be obtained from a preset electromagnetic reflection parameter library according to the material type of the reflection surface. For example, the reflection coefficient of metal material is 0.8-0.95, and that of concrete is 0.3-0.6.

[0044] In the above embodiments, the predicted amplitude of each theoretical reflection path is calculated based on the reflection coefficient and the reflection path length, wherein the predicted amplitude... Free space path loss and reflection loss need to be considered, and their calculation formulas are as follows: ; in, A constant related to transmit power. The wavelength of the Wi-Fi signal. Represented as the reflection path length, The reflection coefficient; The above calculation formula can be simplified as follows: ; in, This is a system calibration constant, the value of which is determined through experimental calibration. Specifically, a known location in the environment can be selected, and the observed amplitude, path length, and reflection coefficient of a known reflection path can be measured. These values ​​can then be substituted into the above calculation formula to obtain the value.

[0045] In the above embodiments, the ratio of the observed amplitude of the observed multipath component to the predicted amplitude of the theoretical reflection path is calculated. Ideally, if the match is correct, this ratio should be close to 1. However, due to factors such as dynamic environmental changes, material parameter errors, and multipath interference, the actual ratio will fluctuate within a certain range. Finally, it is determined whether the ratio is within a preset ratio range. If so, a matching association between the observed multipath component and the theoretical reflection path is constructed. The preset ratio range is usually set to [0.5, 2.0], which allows a deviation of up to 2 times between the predicted amplitude and the observed amplitude. The setting of this range takes into account the following factors: measurement or calibration error of the reflection coefficient, fluctuation of reflection characteristics caused by dynamic environmental changes, coherent superposition effect of multipath signals, and angle dependence of the receiving antenna gain.

[0046] By verifying both time delay matching and amplitude consistency, the correct correspondence between the observed multipath signal and the theoretical reflection path can be identified more accurately, erroneous matches can be eliminated, and the reliability of subsequent positioning calculations can be improved.

[0047] This invention counts the number of theoretical reflection paths with matching associations. When the number of matching theoretical reflection paths exceeds a set threshold, the reflection point location of the matching theoretical reflection path is used as a virtual access point. The virtual ranging value from the moving vehicle to the virtual access point is calculated based on the observation delay of the corresponding observed multipath components with matching associations. The set threshold can be set empirically, but is generally not less than 3, meaning that at least three sets of matched observed multipath components and theoretical reflection paths are required. Since this invention requires that the matching association satisfy the following conditions: each observed multipath component is matched with at most one theoretical reflection path, and each theoretical reflection path is matched with at most one observed multipath component, counting the theoretical reflection paths with matching associations is sufficient.

[0048] The location of the reflection point on the theoretically correlated reflection path is used as a virtual access point. The virtual ranging value from the mobile vehicle to the virtual access point is calculated based on the corresponding observation delay. Here, the virtual access point represents a "virtual signal source" that equates the signal reflection path to directly transmitting a signal from the reflection point to the mobile vehicle. Geometrically, the reflection point decomposes the original Wi-Fi wireless access point → reflector → mobile vehicle reflection path into two segments: one segment is the fixed location from the Wi-Fi wireless access point to the reflection point, and the other segment is the unknown distance from the reflection point to the mobile vehicle. Using the concept of the virtual access point, the second distance segment can be used as a ranging constraint in the positioning equation, effectively placing a real access point at the reflection point location. The virtual ranging value represents the equivalent distance between the mobile vehicle and the virtual access point. Its calculation is based on the observed total path length minus the fixed distance from the Wi-Fi wireless access point to the reflection point. The specific calculation steps are as follows: First, based on the time delay of the observed multipath components... and the speed of electromagnetic wave propagation Calculate the total length of the observation path for this observation multipath component. : ; Then, by subtracting the distance from the Wi-Fi access point to the reflection point from the total length of the observation path, the virtual ranging value can be obtained.

[0049] When there are at least three theoretical reflection paths that are matched and associated, a set of positioning equations is constructed based on the location of each virtual access point and its corresponding virtual ranging value.

[0050] Specifically, constructing the positioning equations involves the following steps: Based on the location of each virtual access point and its corresponding virtual ranging value Establish the virtual ranging constraint equation: ; The geometric meaning of the above virtual ranging constraint equation is: the position of the moving vehicle. To the virtual access point location The Euclidean distance is equal to the virtual distance value. In three-dimensional space, the above equation defines a sphere with the virtual access point as its center and the virtual distance measurement value as its radius. The actual position of the mobile vehicle should be located on this sphere.

[0051] The positioning equation set is formed by combining all the virtual ranging constraint equations: ; in, The coordinates of the mobile carrier to be solved are represented by i = 1, 2, ..., n, where n is the number of theoretical reflection paths with matching associations.

[0052] Geometrically, the location problem is finding... The common intersection of the spheres: When n=1, the system of equations is underdetermined, and the solution is a sphere, so a unique position cannot be determined. When n=2, the system of equations is undetermined, and the solution is the intersection of the two spheres, but the unique position still cannot be determined. When n=3, theoretically the intersection of the three spheres can be 0, 1 or 2 discrete points, which may determine the location. However, if the three virtual access points are collinear, the system of equations is still undetermined. When n≥4, the equation system is overdetermined. Due to the presence of measurement noise, the spheres usually do not have strict common intersections. The optimal position estimate needs to be obtained by optimization using methods such as least squares. When n=3, the positioning equations are theoretically a well-posed problem, but due to the presence of measurement noise, in practice, the optimal position estimate still needs to be optimized by methods such as least squares.

[0053] It should be noted that although the positioning equations are the same in form as the traditional multilateral measurement positioning equations, their sources and physical meanings are different. In the traditional method, the ranging value comes from the direct path measurement of the real access point, while the ranging value in this invention comes from the equivalent ranging of the virtual access point. The validity of this equivalence is based on strict path prediction and matching association.

[0054] The above scheme integrates multiple virtual ranging constraints into a unified set of mathematical equations, providing complete constraints for the final position solution.

[0055] The positioning coordinates of the mobile vehicle can be obtained by solving the positioning equations.

[0056] Solving the positioning equations is a crucial step in transforming mathematical constraints into actual position estimates. Since the equations are often overdetermined and contain measurement noise, optimization methods are needed to find the optimal estimate.

[0057] In one embodiment, the proposed method for solving a system of location equations includes the following steps: Construct a least-squares optimization objective function based on the aforementioned positioning equations: ; The meaning of the constructed least-squares optimization objective function is: to find a position This minimizes the sum of squared residuals between the distance from this location to each virtual access point and the corresponding virtual ranging value.

[0058] By iteratively solving the least squares optimization objective function, the positioning coordinates of the mobile vehicle that minimize the value of the least squares optimization objective function are obtained. Specifically, the initial estimated position is first used as the initial value for iteration. Then, the coordinate values ​​are continuously updated using the gradient descent method until the objective function converges to its minimum value, thus obtaining the positioning coordinates of the mobile vehicle. .

[0059] The method for solving the positioning equation set proposed in the above embodiments assigns the same weight to all virtual ranging constraint equations, but does not consider the difference in the reliability of different virtual ranging values.

[0060] To further improve positioning accuracy, in another embodiment, a weighted least squares solution method is proposed for the positioning equations, including the following steps: The signal-to-noise ratio of the observed multipath components corresponding to each virtual access point is extracted based on the Wi-Fi channel state information. The signal-to-noise ratio (SNR) reflects the intensity of the observed multipath component relative to noise; the higher the SNR, the higher the accuracy of the time delay measurement.

[0061] The latency deviation value corresponding to each virtual access point is obtained as the latency residual. The time delay residual reflects the matching accuracy between the observed multipath component and the theoretical reflection path. The smaller the residual, the more accurate the matching and the more reliable the virtual ranging value.

[0062] The standard deviation of each virtual ranging value is calculated based on the preset environmental measurement error and the preset material parameter uncertainty index. The standard deviation of the distance measurement takes into account multiple sources of error: Among them, environmental measurement error The positional error of the reflective surface in the environmental reflective surface data depends on the accuracy of the lidar, with a typical value of 0.1-0.3 meters. The uncertainty index of the material parameters is also considered. The error in path length estimation is due to the uncertainty of the reflection coefficient, with a typical value of 0.2-0.5 meters.

[0063] Based on the signal-to-noise ratio Distance standard deviation and delay residual Calculate the weighting coefficients used to characterize the reliability of the virtual ranging values ​​of each virtual access point. The weighting coefficient The calculation formula is: ; in, This represents a normalization function used to normalize the weight coefficients to the range of 0-1 and satisfy... , This represents the maximum signal-to-noise ratio (SNR) among all observed multipath components, used to normalize the SNR term. The preset time delay residual threshold is used to control the degree of influence of the time delay residual on the weight.

[0064] Based on the positioning equations and the weighting coefficients Construct the weighted least squares optimization objective function: ; The weighted least squares optimization objective function is solved by an iterative optimization method to obtain the positioning coordinates of the mobile vehicle that minimize the value of the weighted least squares optimization objective function. The iterative optimization method here can be the same as the iterative optimization method in the previous embodiments.

[0065] The difference between the weighted least squares optimization objective function constructed in this embodiment and the least squares optimization objective function constructed in the previous embodiment is that each residual term is multiplied by the corresponding weight coefficient. Constraints with higher weights contribute more to the objective function, and the optimization process will be more inclined to satisfy these constraints with higher weights.

[0066] The two embodiments described above present two methods for solving the positioning equations. The first least squares method is computationally simple and treats all ranging constraints equally. Its advantage is its ease of implementation, but its disadvantage is that it is easily affected by abnormal ranging values, resulting in poor positioning robustness. The weighted least squares method dynamically adjusts the weights based on the ranging quality, prioritizing high-quality ranging measurements. Its advantages are higher positioning accuracy and robustness, especially when the ranging quality varies significantly. Its disadvantage is that it requires additional calculation of weight coefficients, slightly increasing computational complexity.

[0067] It should be noted that when the number of virtual access points is small, i.e., n=3 or 4, the positioning equations are close to well-posed or slightly overdetermined, and the ranging error has a significant impact on the positioning results. In this case, the advantage of the weighted least squares solution method is more obvious, as it can alleviate the problem of insufficient constraints by prioritizing the trust of high-quality ranging. However, when the number of virtual access points is large, the redundancy of the positioning equations increases. Even if the basic least squares solution method is used, some ranging errors can be smoothed out through redundant observations. However, the weighted least squares solution method can still further improve the accuracy.

[0068] Finally, navigation control commands to drive the mobile vehicle's motion are generated based on the obtained positioning coordinates. Navigation control is the step that applies the positioning results to actual motion control, based on the positioning coordinates. Based on the target location or planned path of the mobile carrier, corresponding motion control commands can be generated. This is existing technology and will not be described in detail.

[0069] The above scheme establishes a complete navigation link from Wi-Fi signal observation to motion control of the mobile carrier. The core innovation of this method is to make full use of environmental reflective surface information, transforming multipath signals, which are traditionally regarded as interference, into valuable positioning resources, and achieving more accurate positioning in complex indoor environments where GPS is unavailable and direct paths are blocked.

[0070] In another embodiment, reference Figure 2 A multi-source sensing information fusion autonomous navigation device is provided, comprising: The data acquisition module is used to acquire the current Wi-Fi channel status information, initial estimated location, Wi-Fi wireless access point location, and environmental reflective surface data of the mobile carrier; The reflection prediction module is used to predict at least one theoretical reflection path from the Wi-Fi wireless access point location to the initial estimated location based on environmental reflective surface data and the location of the Wi-Fi wireless access point, as well as the predicted time delay and reflection point location of each theoretical reflection path; The delay extraction module is used to extract the observation delay of each observation multipath component of the mobile carrier based on the Wi-Fi channel state information; The time delay deviation calculation module is used to calculate the time delay deviation value between each observation time delay and each prediction time delay, and to obtain the observed multipath components and theoretical reflection paths whose time delay deviation values ​​are less than the preset deviation threshold. The matching and association module is used to construct the matching and association between observed multipath components and theoretical reflection paths; The virtual ranging module is used to count the number of theoretical reflection paths with matching associations. When the number of theoretical reflection paths with matching associations is greater than a set threshold, the reflection point position of the theoretical reflection path with matching associations is used as a virtual access point. The virtual ranging value from the mobile vehicle to the virtual access point is calculated based on the observation delay of the corresponding observation multipath component with matching associations. The equation construction module is used to construct a set of positioning equations based on the location of each virtual access point and its corresponding virtual ranging value. The equation solving module is used to solve the positioning equation set to obtain the positioning coordinates of the mobile carrier; A designated generation module is used to generate navigation control commands that drive the mobile carrier to the target position based on the positioning coordinates of the mobile carrier.

[0071] In one example, the reflection prediction module predicts at least one theoretical reflection path from the Wi-Fi access point location to the initial estimated location, as well as the predicted delay and reflection point location for each theoretical reflection path. Specifically, this includes: extracting the reflection surface location and normal vector of the target reflection surface based on environmental reflection surface data, where the target reflection surface is the surface that may reflect the signal originating from the Wi-Fi access point location to the initial estimated location; calculating the mirror point location of the Wi-Fi access point location with respect to the target reflection surface using the mirror method based on the reflection surface location and normal vector; calculating the reflection point location of the signal reflected from the target reflection surface to the mobile carrier using geometric intersection based on the mirror point location and the initial estimated location; using the path originating from the Wi-Fi access point location, passing through the reflection point location, and reflecting to the initial estimated location as the theoretical reflection path; calculating the reflection path length of each theoretical reflection path to the initial estimated location based on the Wi-Fi access point location and the reflection point location; and calculating the predicted delay of each theoretical reflection path based on the reflection path length and the signal propagation speed.

[0072] In one example, the matching and association module for constructing the corresponding matching association between the observed multipath component and the theoretical reflection path also includes: for the observed multipath component and the theoretical reflection path whose delay deviation value is less than a preset deviation threshold: extracting the observed amplitude of the observed multipath component based on Wi-Fi channel state information; extracting the reflection coefficient of the reflection surface corresponding to the theoretical reflection path based on environmental reflection surface data; calculating the predicted amplitude of each theoretical reflection path based on the reflection coefficient and the reflection path length; calculating the ratio of the observed amplitude to the predicted amplitude; determining whether the ratio is within the preset ratio range, and if so, constructing the matching association between the observed multipath component and the theoretical reflection path.

[0073] In one example, the equation building module constructs a set of location equations, including determining the locations of each virtual access point. Based on the corresponding virtual ranging value Establish the virtual ranging constraint equation: ; All virtual ranging constraint equations are combined to form a set of positioning equations: ; in, This represents the location coordinates to be solved.

[0074] In one example, the equation solving module solves the system of location equations including: Construct a least-squares optimization objective function based on the positioning equations: ; Iteratively solving the least squares optimization objective function yields the positioning coordinates of the mobile vehicle that minimize the value of the least squares optimization objective function. .

[0075] In another example, the equation solving module solves the localization equations by: extracting the signal-to-noise ratio of the observed multipath components corresponding to each virtual access point based on the Wi-Fi channel state information. ; Obtain the latency deviation value corresponding to each virtual access point as the latency residual. The standard deviation of each virtual ranging value is calculated based on the preset environmental measurement error and the preset material parameter uncertainty index. According to the signal-to-noise ratio Distance standard deviation and delay residual Calculate the weighting coefficients used to characterize the reliability of the virtual ranging values ​​of each virtual access point. Based on the positioning equations and weighting coefficients Construct the weighted least squares optimization objective function: ; The weighted least squares optimization objective function is solved by iterative optimization, yielding the positioning coordinates of the mobile vehicle that minimize the value of the weighted least squares optimization objective function. .

[0076] In the example above, the weighting coefficients in the equation solving module The specific calculation formula is as follows: ; in, Represents the normalization function. This represents the maximum signal-to-noise ratio among all observed multipath components. This is the preset time delay residual threshold.

[0077] Figure 3 The figure shows a block diagram of an electronic device according to an embodiment, such as Figure 3 As shown, the electronic device includes one or more processors and a memory. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the autonomous navigation method for multi-source sensing information fusion provided in any of the above embodiments. The processor may be a central processing unit (CPU) or other processing unit with data processing capabilities and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions. The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0078] In one example, the electronic device may also include input devices and output devices, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0079] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device may include any other suitable components depending on the specific application.

[0080] Embodiments of the present invention may also be computer-readable storage media storing a computer program thereon, which, when executed by a processor, implements the steps of the autonomous navigation method for multi-source sensing information fusion provided in any of the above embodiments. The computer-readable storage medium may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0081] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0082] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0083] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0084] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application should not be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0085] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. An autonomous navigation method based on multi-source sensing information fusion, characterized in that, Includes the following steps: Acquire the current Wi-Fi channel status information, initial estimated location, Wi-Fi wireless access point location, and environmental reflective surface data of the mobile carrier; Based on environmental reflective surface data and the location of Wi-Fi access points, predict at least one theoretical reflection path from the location of the Wi-Fi access point to the initial estimated location, as well as the predicted delay and reflection point location for each theoretical reflection path. The observation delay of each observation multipath component of the mobile carrier is extracted based on the Wi-Fi channel state information; Calculate the time delay deviation between each observation time delay and each prediction time delay, and obtain the observed multipath components and theoretical reflection paths whose time delay deviation values ​​are less than the preset deviation threshold; Construct a matching correlation between observed multipath components and theoretical reflection paths; The number of theoretical reflection paths with matching associations is counted. When the number of theoretical reflection paths with matching associations is greater than a set threshold, the reflection point location of the theoretical reflection path with matching associations is taken as a virtual access point. The virtual ranging value from the mobile vehicle to the virtual access point is calculated based on the observation delay of the corresponding observation multipath component with matching associations. Based on the location of each virtual access point and its corresponding virtual ranging value, a set of positioning equations is constructed, including: based on the location of each virtual access point... and its corresponding virtual ranging value Establish virtual ranging constraint equations: The positioning equation set is formed by combining all the virtual ranging constraint equations, where... The coordinates represent the location of the mobile carrier to be solved. , where n is the number of theoretical reflection paths with matching associations; Solving the positioning equations yields the positioning coordinates of the mobile vehicle, including: The signal-to-noise ratio of the observed multipath components corresponding to each virtual access point is extracted based on the Wi-Fi channel state information. ; The latency deviation value corresponding to each virtual access point is obtained as the latency residual. ; The standard deviation of each virtual ranging value is calculated based on the preset environmental measurement error and the preset material parameter uncertainty index. ; Based on the signal-to-noise ratio Distance standard deviation and delay residual Calculate the weighting coefficients used to characterize the reliability of the virtual ranging values ​​of each virtual access point. ; Based on the positioning equations and the weighting coefficients Construct the weighted least squares optimization objective function: The weighted least squares optimization objective function is solved by an iterative optimization method to obtain the positioning coordinates of the mobile vehicle that minimize the value of the weighted least squares optimization objective function. ; Based on the positioning coordinates of the mobile carrier, navigation control commands are generated to drive the mobile carrier to the target position.

2. The autonomous navigation method based on multi-source sensing information fusion according to claim 1, characterized in that, The Wi-Fi channel state information refers to the multipath component parameters, including signal amplitude, phase, and delay, obtained through the wireless communication protocol; the environmental reflector data refers to the geometric information of the target reflector surface in the environment that can reflect wireless signals.

3. The autonomous navigation method based on multi-source sensing information fusion according to claim 2, characterized in that, Based on environmental reflector data and the location of the Wi-Fi access point, predict at least one theoretical reflection path from the Wi-Fi access point location to the initially estimated location, as well as the predicted delay and reflection point location for each theoretical reflection path, including: The reflective surface position and normal vector of the target reflective surface are extracted based on the environmental reflective surface data. The target reflective surface is a reflective surface that may reflect signals originating from the Wi-Fi wireless access point to the initial estimated position. Based on the position of the reflective surface and the normal vector, the mirror image position of the Wi-Fi wireless access point with respect to the target reflective surface is calculated using the mirror method. Based on the mirror point position and the initial estimated position, the position of the reflection point of the signal reflected from the target reflective surface to the moving carrier is calculated by geometric intersection; The theoretical reflection path is the path that starts from the location of the Wi-Fi wireless access point, passes through the location of the reflection point, and reflects back to the initial estimated location. Based on the location of the Wi-Fi wireless access point and the location of the reflection point, calculate the reflection path length of each theoretical reflection path to the initial estimated location; The predicted time delay of each theoretical reflection path is calculated based on the reflection path length and the signal propagation speed.

4. The autonomous navigation method based on multi-source sensing information fusion according to claim 3, characterized in that, Constructing a matching correlation between observed multipath components and theoretical reflection paths, including: Obtain the observed multipath components and theoretical reflection paths when the time delay deviation is less than a preset deviation threshold; The observation amplitude of the observed multipath component is extracted based on the Wi-Fi channel state information; The reflection coefficient of the target reflecting surface corresponding to the theoretical reflection path is extracted based on the environmental reflecting surface data; The predicted amplitude of the theoretical reflection path is calculated based on the reflection coefficient and the reflection path length; Calculate the ratio of the observed amplitude of the observed multipath component to the predicted amplitude of the theoretical reflection path; Determine whether the ratio is within a preset ratio range. If so, construct a matching association between the observed multipath component and the theoretical reflection path.

5. The autonomous navigation method based on multi-source sensing information fusion according to claim 4, characterized in that, To establish a matching association between observed multipath components and theoretical reflection paths, the following requirements must be met: each observed multipath component can be matched with at most one theoretical reflection path, and each theoretical reflection path can be matched with at most one observed multipath component.

6. The autonomous navigation method based on multi-source sensing information fusion according to any one of claims 1 to 5, characterized in that, The weighting coefficient The calculation formula is: in, Represents the normalization function. This represents the maximum signal-to-noise ratio among all observed multipath components. This is the preset time delay residual threshold.

7. An autonomous navigation device based on multi-source sensing information fusion, used to implement the autonomous navigation method based on multi-source sensing information fusion as described in claim 1, characterized in that, include: The data acquisition module is used to acquire the current Wi-Fi channel status information, initial estimated location, Wi-Fi wireless access point location, and environmental reflective surface data of the mobile carrier; The reflection prediction module is used to predict at least one theoretical reflection path from the Wi-Fi wireless access point location to the initial estimated location based on environmental reflective surface data and the location of the Wi-Fi wireless access point, as well as the predicted time delay and reflection point location of each theoretical reflection path; The delay extraction module is used to extract the observation delay of each observation multipath component of the mobile carrier based on the Wi-Fi channel state information; The time delay deviation calculation module is used to calculate the time delay deviation value between each observation time delay and each prediction time delay, and to obtain the observed multipath components and theoretical reflection paths whose time delay deviation values ​​are less than the preset deviation threshold. The matching and association module is used to construct the matching and association between observed multipath components and theoretical reflection paths; The virtual ranging module is used to count the number of theoretical reflection paths with matching associations. When the number of theoretical reflection paths with matching associations is greater than a set threshold, the reflection point position of the theoretical reflection path with matching associations is used as a virtual access point. The virtual ranging value from the mobile vehicle to the virtual access point is calculated based on the observation delay of the corresponding observation multipath component with matching associations. The equation construction module is used to construct a set of positioning equations based on the location of each virtual access point and its corresponding virtual ranging value. The equation solving module is used to solve the positioning equation set to obtain the positioning coordinates of the mobile carrier; A designated generation module is used to generate navigation control commands that drive the mobile carrier to the target position based on the positioning coordinates of the mobile carrier.

8. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the autonomous navigation method for multi-source perception information fusion as described in claim 1, 2, 3, 4, or 5.

9. A computer storage medium storing computer-executable instructions thereon, characterized in that: When the computer-executable instructions are executed by the processor, they implement the steps of the autonomous navigation method for multi-source perception information fusion as described in claim 1, 2, 3, 4, or 5.

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