51 results about "Map quality" patented technology

The invention discloses an image enhancement and visual SLAM (Simultaneous Localization and Mapping) method and system aiming at a low-illumination scene. The method comprises the following steps: firstly, judging by utilizing an image brightness detection algorithm, and carrying out image brightness adjustment on an image with abnormal current brightness; then carrying out image brightness adjustment based on a mean value adaptive Gamma value, calculating Gamma correction indexes corresponding to different low-illumination images with abnormal brightness, and carrying out brightness correction on the different low-illumination images according to the Gamma correction indexes; and finally, carrying out image contrast adjustment based on a CLAHE algorithm, wherein the CLAHE algorithm is adopted to enhance the contrast of the image after the mean value self-adaptive Gamma correction. According to the method, the problems of poor positioning precision, low robustness, poor map quality and the like of a visual SLAM system in a low-light environment in the prior art are solved. According to the method, deep learning and a traditional visual SLAM framework are combined, image performance is changed through an image enhancement method to improve image quality, and a feature point extraction mode and matching precision in a low-light environment are improved.

The invention discloses a semantic vision SLAM (Simultaneous Localization and Mapping) method and a semantic vision SLAM system for an indoor low-texture and dynamic environment, which are characterized in that a visual odometer, a dynamic point elimination algorithm, a loopback detection algorithm and the like are integrated into a visual SLAM system, so that data in an indoor low-texture and dynamic scene are processed in real time, and high-precision positioning and high-quality map construction are provided. A semantic information acquisition thread and a dynamic point elimination module are added to the whole system, and a loopback detection module is improved. According to the method, the problems of poor positioning precision, low robustness, poor map quality and the like of a visual SLAM system in a low-texture environment and a dynamic environment in the prior art are solved, and the feature point extraction and matching precision in the low-texture environment is improved; the interference of a dynamic object on the SLAM system is eliminated, and the positioning precision and the loopback detection performance are improved; the map construction quality is improved, and the semantic map with high readability is constructed by fusing semantic information.

The invention provides a map data acquisition crowd sensing participant recruitment method and a related system, and relates to the technical field of intelligent traffic. According to the method, a vehicle dynamic perception utility evaluation mechanism and a vehicle historical task reputation reuse mechanism are combined under an edge calculation framework, and an actual map defect area existing in a target map construction area in charge of each edge node is constructed; intelligent vehicles with sufficient perception utility intensity and good reputation are recruited as required for collaborative perception mapping, so that the timeliness, the accuracy and the system robustness of map data acquisition operation in a complex dynamic traffic environment are effectively enhanced, and the overall timeliness, the overall efficiency and the map quality of crowd-sourcing collaborative perception mapping operation are improved; the problems of local area sensing redundancy, local area sensing deficiency, serious mapping communication delay and the like existing in an existing crowd-sourcing cooperative sensing mapping scheme can be further relieved.

A map quality assessment system that assesses an error associated with primary map data, the map quality assessment system comprising one or more central computers that execute instructions to determine an error associated with the primary map data, an error associated with the primary map data is compared to a range of values defined by the one or more quality metric values, and in response to determining that the error associated with the primary map data falls within the range of values defined by the one or more quality metric values, a template for selecting the primary map data is retained. In response to determining that an error associated with the primary map data falls outside of a range defined by the one or more quality metric values, the one or more central computers evaluate the primary map data for real anomalies within the one or more roads represented by the primary map data.

The present invention belongs to the technical field of high-precision map quality evaluation for autonomous driving. The present invention provides a crowdsourced map evaluation method and device based on lane lines, wherein the method includes: obtaining a sequence of lane line points based on crowdsourced data, using a custom distance calculation method between lane lines, hierarchically clustering the lane lines collected multiple times to obtain multiple clusters; for each cluster, performing polynomial fitting on the lane lines within the cluster, calculating the distance from each lane line to the fitted lane line, and mapping the distance to between 0 and 1 using a custom function to obtain the cluster-internal confidence; and calculating the global confidence of the map based on the confidence within each cluster and the total length of the lane lines. In the absence of a true map value, the present invention performs relevant calculations and processing on the lane lines to obtain the local and global confidence of the crowdsourced map, thereby respectively evaluating the local and global quality of the crowdsourced map.

Autonomous docking systems and methods receive asynchronous images from a vehicle-mounted camera and a plurality of infrastructure cameras and perform object mapping to determine candidate routes leading to a docking station. Candidate routes are divided into route segments for which disparity map quality scores are estimated to select a navigation route. Information from infrastructure cameras and vehicle-mounted cameras is used to generate a disparity map to perform object detection, distance measurement, or localization along the navigation route. The disparity map is used to communicate navigation commands to an HMI or a vehicle control system.

The invention discloses a semantic vector map construction method supporting beyond visual range and dynamic updating, and belongs to the technical field of semantic vector map construction method.The method comprises the steps that a look-around image is input into a network, dynamic map updating is achieved through a query propagation mechanism according to the time sequence fusion features, the consistency of map elements in the time dimension is ensured, and the map quality is improved. According to the method, the perception and decision-making capability of the automatic driving system in a dynamic environment can be effectively improved, and accurate and stable map support is provided for the automatic driving technology.

The application provides a multi-type map-based fusion positioning method and an electronic device. The method comprises: acquiring a target image to be positioned; acquiring a target position at which the target image is captured; determining a target map quality of a map located at the target position in a map database composed of multiple different types of map data; determining a target positioning mode from a plurality of different positioning modes preset according to the target map quality, the target positioning mode being composed of basic positioning modes corresponding to each map data included in the map database; and positioning the target image by using the target positioning mode to obtain 6DOF of the target image. Thus, when the image is positioned, a suitable positioning scheme can be selected from multiple positioning schemes according to different map qualities corresponding to positions of the image, a positioning effect with higher precision and stability is realized, map quality is upgraded without perception in time and space, and user experience is improved.

Provided in the embodiments of the present application are a map quality evaluation method and apparatus, and a device and a storage medium. The method comprises: acquiring a map on the basis of perception information collected by means of a perception device; and on the basis of the occurrence of a score deduction scenario in the map, determining a score of the map, wherein the score of the map is used for representing the quality of the map, and the occurrence of the score deduction scenario is inversely proportional to the quality of the map. In the embodiments of the present application, after studying and obtaining a map, a memory navigation system performs quality evaluation on the map, obtains the score of the map by means of the quality evaluation, and displays the score of the map to a driver, such that the driver can determine, by means of the score of the map, whether to use the map, thereby ensuring that each map used by the memory navigation system is a map of a relatively high quality, ensuring the use effect of a memory navigation function, and improving the user experience.

The embodiment of the invention provides a map quality detection method, electronic equipment and a storage medium, and the method comprises the steps: obtaining the trajectory data of a traffic participant in a preset scene, a predetermined true value map and a to-be-detected vectorized map, matching the trajectory data with a first map element in the true value map, obtaining labeled trajectory data, and storing the labeled trajectory data in a database; and determining a target trajectory where the first map element in the labeled trajectory data is matched with a second map element in the vectorized map, obtaining a trajectory subset of the second map element, performing space matching degree and time consistency detection on the trajectory subset of the second map element and the second map element, and obtaining a dynamic detection result of the vectorized map. And determining a quality detection result of the vectorized map according to the dynamic detection result. Quality detection is performed by integrating the dynamic characteristics of the map, and the accuracy of the map quality detection result is improved.

The application discloses a kind of fusion navigation and reliability quantitative evaluation system based on pre-set sparse map.The system is fused positioning module by multi-source sensor, and is fused vision, inertia and optical flow data based on pre-set sparse feature map;Reliability quantitative evaluation module is used to calculate feature distribution balance degree, trajectory coverage and feature stability index, and generate navigation map quality index;Decision control module divides reliability level according to navigation map quality index, dynamically adjusts unmanned aerial vehicle flight speed and task mode;Abnormal processing module triggers degradation flight and monitors pose uncertainty when signal source fails, executes return or hovering strategy when threshold is exceeded.The application realizes high robustness positioning and hierarchical safety control of unmanned aerial vehicle in complex environment.

The invention discloses a grid map processing method, related equipment and a computer program product, and the method comprises the steps: converting an original grid map into a tri-state graph, carrying out the binarization processing, and extracting the contour line of an obstacle from a binarized image; and based on the contour line of the obstacle in the binarized image, performing masking processing on the tri-state graph, and determining an editable gap region in the tri-state graph through a masking form. And screening a first gap region meeting a gap credibility condition, and editing a grid state corresponding to the first gap region in the tri-state graph into an idle state. According to the invention, the accuracy of identifying passable gap areas such as door slots, corridors, narrow channels and the like is improved, and the map quality is improved.

Autonomous docking systems and methods receive asynchronous images from a vehicle-mounted camera and a plurality of infrastructure cameras and perform object mapping to determine candidate routes leading to a docking station. Candidate routes are divided into route segments for which disparity map quality scores are estimated to select a navigation route. Information from infrastructure cameras and vehicle-mounted cameras is used to generate a disparity map to perform object detection, distance measurement, or localization along the navigation route. The disparity map is used to communicate navigation commands to an HMI or a vehicle control system.

The disclosure relates to a method and an apparatus for generating a 3D signal quality map. According to an aspect of the disclosure, there is provided a method for generating a 3D signal quality map, performed by a computing device including at least one processor, the method including: initializing a signal quality map; collecting signal quality values from a plurality of points; estimating a map quality state using the collected signal quality values; determining a quality map parameter based on the estimated map quality state; and updating the signal quality map using the quality map parameter.

The invention provides an efficient map element quality inspection AI system and an implementation method, and relates to the field of map element quality inspection systems, and the system comprises a multi-source data access module which is used for importing multi-type map element data and unifying a spatial reference system; the AI quality inspection model training module comprises a spatial topological feature extraction network and a quality inspection rule self-learning sub-model and is used for generating a dynamic rule base; the multi-element intelligent verification module integrates a topology verification unit, an attribute verification unit and an incidence relation verification unit and is used for joint verification of multiple types of map elements; the visual report and rectification module is used for generating a structured quality inspection report and supporting one-key rectification; and the system management module is used for user authority management and model iteration maintenance. According to the method, eight common error types in map drawing can be automatically identified, and efficient, accurate and intelligent map quality inspection is realized through multi-modal data in combination with deep learning and knowledge reasoning.

The present application relates to the technical field of robot patrol surveying, in particular to a four-legged robot autonomous patrol navigation control and active three-dimensional mapping method, system and terminal. The robot of the present application explores environment data in unknown environment, and draws a map with the explored environment data, so as to realize the purpose of exploring and mapping at the same time, and evaluate the map quality in real time, and actively map the area on the map again whose quality does not meet the standard, so as to improve the quality of the final obtained map. And based on the map quality evaluation result, the path required for exploring the area again whose quality does not meet the standard is planned, so that the robot can accurately and efficiently move to the area whose quality does not meet the standard.

The invention discloses a map quality detection method, a control device, a mobile robot and a medium. The method comprises the following steps: acquiring a current map constructed by the robot; pixel matching is carried out on the current map and a historical map to obtain similarity, and / or feature point matching is carried out on the current map and the historical map to obtain feature point pairs, and the historical map is a map constructed according to historical environment data collected by a robot; determining a map folding condition of the current map according to the similarity and / or the feature point pairs; and determining the map quality of the current map according to the map folding condition. In the embodiment of the invention, the quality of the map can be improved, and the situation of map overlapping is avoided.

The invention relates to the technical field of large liquid container cleaning, in particular to a large liquid container cleaning robot synchronous positioning and mapping method, system and device based on adaptive weight correction vector synthesis, and aims to solve the problems of positioning errors and map distortion of SLAM in a large liquid container environment. According to the method, the single-ring characteristic of a large liquid container is utilized, a flat container wall is selected as a mapping origin and a loopback point, and the influence of incomplete complex feature modeling is avoided; the method comprises the following steps: preprocessing laser radar and odometer data to generate a scanning matching sample and score, and performing principal component analysis on a high-score sample to identify an error direction; self-adaptively synthesizing a correction vector weight according to the sample dispersion degree and the score to realize the accurate correction of the pose; in the loop detection stage, the same adaptive strategy is adopted to optimize matching, and the global map consistency is ensured. The underwater environment positioning precision and the map quality are effectively improved, and the robot cleaning efficiency is enhanced.

The present disclosure provides a model processing and high-definition map quality verification method and device and a storage medium, relating to the fields of artificial intelligence such as automatic driving, machine learning and computer vision, wherein the model processing method can comprise: constructing a Bayesian network model; determining the values of each variable in the Bayesian network model according to the obtained training samples; and determining the network parameters of the Bayesian network model according to the training samples and the values of each variable, wherein the network parameters are used to determine the quality verification result of the high-definition map to be verified, and the quality verification result is determined according to the input data and the network parameters determined based on the point cloud data corresponding to the high-definition map. The scheme disclosed in the present disclosure can save labor and time costs and improve the accuracy of the verification result.

The application provides a micro-map personalized layout information mining method, relates to the related professional knowledge of image processing and cartography, and solves the problem of difficult mapping caused by the lack of professional map knowledge of ordinary people; the method comprises the following steps: obtaining the combination characteristics and position characteristics of map layout elements, obtaining the relationship characteristic information between the layout elements, and constructing a micro-map layout knowledge base; the map component personalized layout information mining method can effectively obtain the distribution characteristics of the map components in the map, provide help for the ordinary people who lack mapping basis, and improve the map production efficiency and map quality.

The invention discloses a map updating method and device, an autonomous mobile device and a storage medium, and relates to the technical field of autonomous navigation. Acquiring a pre-constructed global map, a semantic map, a pose of the autonomous mobile device, and sensor data, in which a static region and a dynamic region in the global map are labeled in the semantic map (S110); according to the pose and the sensor data, periodically constructing a local map to limit the coverage area of the local map (S120); aligning the global map and the local map to obtain a target local map aligned with the global map (S130); and updating the global map according to the global map, the semantic map and the target local map (S140). The consistency of the global map can be ensured, the quality of the global map is improved, and the efficiency and accuracy of map updating are improved.

The application discloses a kind of vehicle autonomous return control method, device and medium without satellite navigation, the method includes the following steps: to unmanned vehicle executes before departure preparation inspection operation, obtains preparation inspection result;According to preparation inspection result, unmanned vehicle is controlled to carry out journey detection;According to the feedback data of unmanned vehicle in journey detection, obtain the driving vehicle condition data and environmental detection data of unmanned vehicle;According to driving vehicle condition data and environmental detection data, point cloud map is constructed;The map quality of point cloud map is judged, and according to map quality, unmanned vehicle is selected to execute self-driving return operation or phased return operation;The application can be when unmanned vehicle investigates and explores road, according to the vehicle condition data of unmanned vehicle, intelligently infers the location of unmanned vehicle, and when returning, according to the aforementioned vehicle condition data and inference data, intelligently control unmanned vehicle to enter adaptive return trajectory and autonomous return mode of environment, realizes the autonomous road exploration return of unmanned vehicle under satellite system and satellite signal without needing.

The invention relates to the technical field of computer vision and robotics, in particular to an SLAM point cloud map-oriented multi-index assessment method, which realizes accurate registration of a to-be-assessed map and a true value map by adopting a point-to-plane ICP algorithm, and further performs voxelization processing based on dynamic size on the registered point cloud, so that the accuracy of the SLAM point cloud map-oriented multi-index assessment is improved. And establishing a Gaussian distribution model in each voxel, finally evaluating global geometric consistency by calculating a voxelization average Wasserstein distance, and evaluating local structure consistency in combination with a spatial consistency score (SCS). A unified quality evaluation standard is established, so that the map quality of different SLAM algorithms has comparability; the adaptive capacity to noise, sparse point cloud and non-uniform sampling is improved, and the robustness of the method is enhanced; the calculation complexity is reduced, good balance between efficiency and precision is achieved, and the requirement for efficient evaluation of large-scale point cloud data in practical application can be met.

The present application relates to the technical field of automated driving, and particularly, provided are a map quality test method, a computer-readable storage medium and an intelligent device, which aim to solve the problems that existing high-definition maps possibly have errors and thus affect safety of driving. For this purpose, the map quality test method of the present application comprises: on the basis of map data of a target area, acquiring first attribute information of a map element to be tested; on the basis of sensed data of the target area, acquiring second attribute information of a reference map element; associating the reference map element with the map element to be tested; comparing the first attribute information with the second attribute information; and at least according to the comparison result, determining a map quality test result of the target area. The method performs map quality test in view of sensed data of a target area, so as to effectively determine in time whether map data is qualified, thereby helping to improve safety of driving.

The invention discloses an intelligent driving map verification method and system, relates to the technical field of intelligent driving, and discloses an intelligent driving map verification method comprising the following steps: collecting current road section data in real time according to a selected verification road section model; acquiring geographic information of feature points based on the current road section data; performing differential matching on the geographic information of the feature points in combination with an intelligent driving map to generate a matching result; and verifying the quality of the intelligent driving map based on the matching result. By selecting a representative verification road section model to collect data in real time, testing on irrelevant or repeated road section features is avoided, and the actual condition of a current road is reflected. The matching process is simplified by extracting key feature points, and the test efficiency is improved; and finding the difference between the intelligent driving map and the actual road through difference matching. The quality of the intelligent driving map is verified based on differences, and the accuracy and integrity of map data are efficiently verified.

The invention belongs to the technical field of city management, and particularly relates to a geographic map establishing method, which comprises the following steps of: 1, determining targets and demands: determining the targets: firstly, determining the purposes and purposes of map making, such as teaching display, city planning, tourism guide, scientific research and the like, and analyzing the demands, the geographic map establishing method analyzes geographic elements (such as terrains, water systems, traffic, places of residence and the like) contained in the map and the detailed degree of the elements, and has the beneficial effects that the system emphasizes the independence and interdependence of each step, and requires careful consideration and strict execution in each stage, so that the efficiency of establishing the geographic map is greatly improved. And meanwhile, the system pays attention to the accuracy of the data and the practicability of the map so as to meet the requirements of different users, and through the system, the production of the geographic map becomes more standard, efficient and reliable.

The invention discloses a hybrid map construction method and device for mobile robot navigation and a medium, and relates to the technical field of navigation hybrid map construction, and the method comprises the following steps: collecting a multi-modal sensor observation flow, constructing a measurement representation, a topological representation and a semantic representation, and generating a time version chain starting point time version; updating the metric representation, the topological representation and the semantic representation in a control period, generating a differential representation, writing the differential representation into a time version chain, and generating a continuously expanded time version chain; generating a structure stability degree, a semantic drift degree, a topological evolution consistency degree and an updating cost density quality curve, and obtaining a quality curve set; and selecting a current running hybrid map time version, performing local updating of measurement representation, semantic representation and topological representation, and adjusting a time window length and a time version switching strategy. The objectivity of map quality evaluation and the time version selection decision precision are improved, and the map updating efficiency and the overall operation efficiency are improved.

This disclosure provides a method, apparatus, and device for simulating and testing the quality of high-precision maps, relating to the field of autonomous driving technology, specifically the field of simulation testing technology. The specific implementation scheme is as follows: determining the simulation type corresponding to the map to be tested; generating a simulation scene corresponding to the map to be tested based on the simulation type; controlling the driving of a simulated vehicle in the simulation scene and collecting abnormal driving information of the simulated vehicle; and generating a quality detection result for the map to be tested based on the abnormal driving information. This implementation method can improve the accuracy of map quality detection.

An operation control method and apparatus for a game map, and an electronic device. The method comprises: displaying a map picture of a game map on a graphical user interface, wherein the map picture comprises a game scene area to be edited, and the game map belongs to a game account logged in on a first terminal device (S102); in response to an area selection operation, determining a target area on the game map, and displaying an area identifier of the target area, wherein the target area is less than the game scene area to be edited (S104); and in response to an account invitation operation, determining a target game account, and sending, to the target game account, invitation information for editing the game map, wherein the invitation information is used for indicating: after the target game account accepts the invitation information, the target game account has an editing permission for editing the target area (S106). The method ensures the dominant role of the game account to which the game map belongs, improves the game experience when multiple players build the game map together, and improves the map quality of the game map.