24619results about "Electromagnetic wave reradiation" patented technology

A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions. Quality metrics guide network weight adjustments to improve reconstruction accuracy while maintaining robustness to sensor failures and environmental variations.

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention discloses a logistics robot path planning method based on multi-modal perception, and relates to the technical field of robot path planning. Laser radar, visual camera and IMU data are fused, and environment state feature vectors are generated through multi-modal data synchronization and space-time alignment; the method comprises the following steps: analyzing environmental semantics by using models such as PointPill and YOLOv8, extracting dynamic characteristics, and identifying obstacles; constructing a space-time risk field, searching a path by a space-time algorithm, converting path points into a continuous trajectory, and optimizing the continuous trajectory; deviation is evaluated in real time, dynamic re-planning is triggered, and multi-robot cooperation and environment semantic understanding are included. Through multi-mode perception fusion, hierarchical planning, multi-target optimization and a cooperation mechanism, the obstacle detection accuracy and the obstacle avoidance success rate are improved, the path planning time is shortened, the energy consumption is reduced, the multi-robot conflict is reduced, the task efficiency is improved, the environment semantic understanding and task adaptive ability is enhanced, and the method is suitable for scenes such as intelligent storage and the like and has wide application prospects. The automation level is improved.

The invention discloses a topographic surveying and mapping system and method of an unmanned aerial vehicle, and belongs to the technical field of topographic surveying and mapping. The system comprises the following modules: a data acquisition module which is responsible for acquiring high-precision terrain and environmental parameter data of a target area by using an unmanned aerial vehicle platform; the data fusion and correction module is responsible for fusing multi-source data and meteorological information and eliminating errors among sensors; the multi-dimensional feature analysis and recognition module is used for performing feature extraction and classification on data of each sensor through multi-dimensional feature analysis, and recognizing and separating an environment interference signal and a real building deformation signal; the interference elimination and deformation extraction module is used for carrying out pattern recognition and quantitative analysis on periodic non-deformation interference and accurately extracting real deformation characteristics of a building from complex dynamic noise; the real-time analysis module is used for realizing real-time data processing and feedback and ensuring that high-precision building change information is quickly provided; and the visualization module provides multi-dimensional dynamic visualization display including a building change heat map, an interference distribution map and a time sequence curve.

The invention relates to the technical field of road crack detection, in particular to a road crack detection method and system based on a fused image. The method comprises the following steps: acquiring road multi-source monitoring data including a visible light image, infrared thermal imaging data and laser radar point cloud data, and performing multi-modal image fusion and road three-dimensional point cloud reconstruction to generate a fused road image and road three-dimensional modeling data; performing crack curvature analysis based on the fused road image to generate crack curvature data; performing reflection crack contour recognition and positioning on the fused road image through the crack curvature data to generate reflection crack initial positioning data; obtaining road base material data; and performing reflection crack stress field reconstruction on the road area according to the reflection crack initial positioning data to obtain a reflection crack stress field. According to the invention, through multi-modal fusion, curvature identification, stress field modeling and crack channel analysis, the accuracy and strain of road reflection crack detection are improved.

The invention belongs to the technical field of unmanned aerial vehicle intelligent navigation, and discloses a low-altitude economic unmanned aerial vehicle data processing method and system based on a large model, and the method comprises the steps: calculating the scene adaptation weight of multi-modal data through a dynamic attention mechanism based on a space-time alignment feature package, and carrying out the fusion to generate a multi-modal joint feature matrix; based on the multi-modal joint feature matrix, constructing a three-dimensional topological model of an urban airspace, predicting a dynamic obstacle trajectory in combination with a space-time diagram neural network, and generating a hierarchical navigation instruction set; the unmanned aerial vehicle executes a flight instruction according to the hierarchical navigation instruction set, and generates a flight state monitoring log by collecting data in flight of the unmanned aerial vehicle in real time; according to the method, the multi-modal joint feature matrix is generated through environment parameter driving weight distribution, and the complex scene sensing precision is remarkably improved.

The invention discloses a multi-source information collaborative power equipment three-dimensional temperature field construction method. Firstly, an infrared camera, an IMU and a laser radar are utilized to obtain an accurate external parameter relation through joint calibration, point cloud distortion is eliminated, angular points and plane points are extracted, a re-projection residual error, a distance sampling residual error and an IMU pre-integration residual error are constructed, an error state iteration Kalman filter is adopted to optimize a global pose, and positioning is achieved. Providing a self-supervised depth completion network, combining an infrared temperature image and a sparse depth map generated by a laser radar as input, adopting a depth completion strategy guided by an infrared image, estimating relative motion of adjacent frames by using pose information, introducing a feature alignment module to reduce alignment errors, and combining the depth map, the infrared image and IMU data to obtain a self-supervised depth completion algorithm; and efficient construction of the three-dimensional temperature field of the power equipment is realized. According to the invention, the three-dimensional temperature field of the power equipment is constructed more accurately, and the capability of the substation inspection robot for state monitoring and fault diagnosis of the power equipment is improved.

The invention relates to the technical field of building appearance defect detection, in particular to a building appearance defect detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the building information of a target building, and generating a hierarchical scanning path and a three-dimensional obstacle avoidance flight path, a visible light image, an infrared thermodynamic diagram and laser radar point cloud information are collected for space-time alignment processing, and an attention mechanism neural network is used for extracting multi-scale features to generate a detection report containing defect three-dimensional coordinates, damage levels and safety risk assessment. The method achieves the purpose of efficiently and accurately detecting the building appearance defects, can adapt to complex building structures and environmental conditions, supports defect trend prediction and maintenance decision, and remarkably improves the building safety management efficiency.

The invention relates to a multi-sensor cross-scene dynamic preferential fusion positioning and mapping method, and the method comprises the steps: obtaining the data of a plurality of sensors, and completing the unification of the time-space relation of the data of the plurality of sensors; processing the data, carrying out loopback detection on image key frame data acquired by a camera, constructing to obtain an I MU pre-integration factor, a visual inertial odometer factor, a laser radar odometer factor, a GPS inertial odometer factor, a UWB factor, a GNSS factor and a loopback detection factor, and adding the factors into a factor graph for optimization; a global positioning pose and a map are obtained; and optimizing the multi-sensor data fusion strategy based on a deep fuzzy neural network. According to the multi-sensor cross-scene dynamic preferential fusion positioning and mapping method provided by the invention, high-precision positioning and navigation of an agricultural robot in different scenes are realized through real-time fusion of various sensor data, and the problems of scene dependence and insufficient precision of an existing single sensor scheme are solved.

The invention relates to the technical field of software systems, and particularly discloses a vehicle matching and positioning system and method based on comprehensive characteristics of a vehicle and a container, and the system comprises a multi-mode perception and intelligent identification module, a dynamic task matching and scheduling optimization module, an intelligent decision and automatic execution module, and a user interaction and operation module. According to the system, the states of a container and a container truck are sensed in a fusion mode through multiple sensors, cross-modal data feature learning is carried out through a Transform-VIM self-attention mechanism, and high-precision container recognition in a complex environment is achieved; meanwhile, based on multi-dimensional feature matching and a Hungary optimization algorithm, a dynamic task matching mechanism is constructed, and it is ensured that the container trucks and the containers are in accurate butt joint; the system optimizes a scheduling strategy through reinforcement learning, dynamically adjusts an operation process, and is linked with automatic equipment, so that intelligent and efficient port container transportation management is finally realized, the risks of wrong loading, neglected loading and operation delay are effectively reduced, and the overall throughput and operation efficiency of a port are improved.

The invention discloses a multi-modal image automatic labeling system and method, and relates to the technical field of image data processing. According to the multi-modal image automatic labeling system and method, time sequence alignment is carried out on video streams and laser radar point cloud data through an asymmetric dynamic time warping algorithm, semantic and geometric features are extracted, and the elastic coefficient of the algorithm is dynamically adjusted. And combining a modal perception attention mechanism, dynamically allocating fusion weights of the video stream and the laser radar according to the features, generating a cross-modal joint feature vector, and outputting a preliminary labeling result. And generating an annotation robustness index by calculating the prediction entropy and the three-dimensional intersection-to-union ratio confidence of the target detection frame, and iteratively optimizing the annotation result. And mapping the cross-modal features and the labeling result into a space-time correlation map, and displaying the three-dimensional positioning, motion trail and modal contribution degree thermodynamic diagram of the target in real time. The problems of time alignment, feature fusion and labeling robustness are effectively solved, and a high-precision and interpretable automatic labeling solution is provided.

The present invention relates to a method and system for estimating forest carbon storage that combines artificial intelligence algorithms and multimodal remote sensing data. This approach comprehensively utilizes laser radar satellites, multi- / hyperspectral satellites, radar satellites, high-resolution optical imagery, etc. A hybrid technical system is employed for different forest coverage areas, resulting in high-precision forest carbon storage mapping with a resolution of 10 meters and area coverage. This provides technical support and assurance for assessing global forest carbon storage and supporting forestry carbon sequestration transactions.

The invention provides a forest land tree height measurement and determination method and system based on laser radar point cloud data. Stress wave signals are collected based on a trunk base acoustic emission sensor array to generate an acoustic characteristic parameter set, the digital twin model is driven to complete forest stand structure topological optimization, and a three-dimensional growth vector model reflecting the internal mechanical state of a trunk is formed. And synchronously fusing high-precision slope point cloud data returned by the unmanned aerial vehicle laser radar in real time, correcting a terrain distortion error through a dynamic splicing algorithm in combination with stress distribution characteristics, and generating a crown segmentation boundary constrained by physical characteristics. And finally outputting a tree height parameter corrected by the abrupt slope topography through model iterative optimization and space vector analysis. According to the technical scheme, synchronous sensing of the three-dimensional shape and the mechanical state of the tree in the complex terrain environment is achieved, and the tree height measurement error is reduced.

The invention relates to the field of virtual reality positioning, and discloses a VR large-space positioning interaction system based on multi-modal perception, and the system comprises the steps: deploying a multi-modal sensor to obtain sensing data, carrying out the visual feature extraction and preprocessing, building a sparse point cloud map in a matching manner, carrying out the scale calibration, and constructing an environment model; pre-judging a UWB signal path based on an environment model, performing error optimization compensation on an NLOS state, and performing observation updating and fusion through degradation detection to obtain a predicted state change; constructing an interactive perception network, and tracking the hands and the whole body; tactile feedback is realized by using a layered tactile system, and a tactile effect is generated by using vibration frequency mapping; the transmission efficiency is improved by using a beam forming technology, an edge cloud server cluster renders a virtual scene, and the scene is pre-rendered in advance to offset network and rendering delay; an online calibration mechanism is designed, and system errors are corrected through visual loopback detection, UWB beacon dynamic correction and IMU drift compensation.

The invention relates to the field of forest protection, and discloses an unmanned aerial vehicle intelligent monitoring system for forest protection. Comprising a multispectral data dynamic acquisition module, an environment field real-time modeling module, an image intelligent enhancement module, a multi-scale frequency domain feature extraction module, a Bayesian anomaly probability inference module, a space-time correlation verification module, an unmanned aerial vehicle cluster scheduling module and a heterogeneous hardware acceleration module. According to the system, real-time monitoring and accurate identification of the hidden ecological damage behavior are realized through a closed-loop process of multi-modal sensor collaborative acquisition, environmental parameter dynamic prediction, image illumination invariance transformation, cross-scale texture fingerprint extraction, particle filter dynamic threshold optimization, multi-constraint space-time clustering, resource allocation optimization and reconfigurable hardware acceleration. According to the invention, on the basis of an image preprocessing architecture based on illumination reflection separation and edge preserving enhancement, the feature distortion bottleneck of a traditional algorithm in a complex illumination environment is broken through, and a high-fidelity data base is provided for hidden ecological damage detection.

The invention discloses a navigation matching correction method based on an inspection robot, and the method comprises the steps: building a feature database through the deployment of a physical calibration object and the recognition of a natural feature object, providing a reliable positioning reference for a robot, achieving the coarse positioning through the fusion of visual and laser radar data in a positioning process, and introducing a dynamic credibility evaluation mechanism. The positioning reliability is quantified in real time through an exponential decay model, when the credibility is lower than a threshold value, the system automatically triggers a compensation behavior to re-search features, in the aspect of multi-robot cooperation, secondary positioning correction is achieved through track matching and data fusion, high-confidence-coefficient reference data are screened through a clustering algorithm, the group positioning precision is improved, and the positioning accuracy is improved. Aiming at a key inspection area, multi-angle image matching is adopted to realize fine positioning, a positioning error is dynamically corrected through a sliding window, the continuity and accuracy of robot navigation in a complex environment are remarkably improved through closed-loop correction and self-adaptive optimization, and the method is suitable for intelligent inspection requirements of railway trains.

Object detection and tracking systems may use machine-learned transformer models with self-attention for detecting, classifying, and / or tracking objects in an environment. Techniques described herein may include receiving sensor data generated by different sensor modalities of a vehicle, determining different bounding shapes based on the different sensor modalities, and using a machine-learned transformer model to determine associated and / or combined bounding shapes. The machine-learned transformer model may receive a variable number of input bounding shapes representing any number of objects and various sensor modalities. Multiple stages of the transformer may be used to determine associated bounding shapes and to assign attributes for the associated bounding shapes, based on the individual bounding shapes of the different sensor modalities and / or previous bounding shapes for objects detected and tracked in a previous scene in the environment.

The invention relates to a man-machine hybrid autonomous navigation system in an unknown dynamic environment, which comprises an environment sensing module for acquiring environment information by using sensors such as a laser radar, a camera and an inertial measurement unit and performing data preprocessing; the local target point selection module selects a local target point set meeting requirements in the environment according to the real-time information provided by the environment sensing module; the decision planning module is used for generating an optimal path and a navigation strategy based on mixed training of a deep reinforcement learning algorithm and artificial experience; and the learning optimization module improves the generalization ability of the algorithm through online learning and transfer learning technologies, and realizes effective introduction and strategy adjustment of human experience in combination with the man-machine interaction module. The man-machine interaction module can work in cooperation with the local target point selection module so as to dynamically adjust target point selection according to external input and optimize path planning. According to the method, high decision stability can be kept in an unseen environment, the training cost is reduced, and the method is more suitable for autonomous navigation application in the real world.

The invention discloses a robot dynamic environment adaptive sensing and navigation system based on a three-dimensional laser radar, relates to the technical field of robots, and solves the technical problems that comprehensive environment information is difficult to obtain, and the weight is difficult to adjust by fusing weather types and sensor confidence coefficients. Comprising the following steps: generating original point cloud data by combining a bionic compound eye type laser radar with a silicon photon integrated chip; marking point clouds based on a KITTI data set, performing preprocessing, training a Transform model to output a dynamic obstacle mask, and filtering background point clouds; a weather detection model is constructed, the weight is dynamically adjusted according to the weather type and the sensor confidence coefficient, and position and attitude estimation is fused; laser radar point cloud constructs a geometric map, a camera depth map generates a dense map, and semantic tags are mapped to generate an environmental semantic map; and converting the environmental semantic map into a three-dimensional grid map, and planning an obstacle avoidance path based on the grid map by using an A * algorithm.

The invention discloses an obstacle detection method and an obstacle detection system for a track, relates to the technical field of track detection, and is used for solving the problem of insufficient safety of track traffic. Comprising a data acquisition module, a data storage module, a data processing and fusion module, an intelligent analysis and decision module and an execution module which are in signal connection. The data acquisition module dynamically adjusts the working range and frequency of the sensor according to environmental changes. The collected data is processed and fused through the data processing and fusion module to generate a consistent orbit environment model, and a high-precision obstacle model is generated in combination with multi-source data. The intelligent analysis and decision-making module carries out classification, tracking and risk assessment on obstacles and generates a reasonable coping strategy according to the risk level. And the execution module executes related operation according to the generated strategy instruction. According to the method, track obstacles can be accurately detected, self-adaptive adjustment can be carried out according to dynamic environment changes, and the safety and operation efficiency of track traffic are remarkably improved.

The invention belongs to the technical field of three-dimensional imaging and measurement, and particularly discloses a laser scanning three-dimensional imaging method and device, a medium and equipment, and the method comprises the steps: obtaining laser scanning data of a target object, and the laser scanning data comprises depth data including reflection intensity, a scanning angle, a timestamp and flight time; preprocessing the laser scanning data, and fusing a time sequence and scanning path information to obtain multi-channel input features adaptive to a neural network; constructing a three-dimensional geometric reconstruction model, and training the model; inputting the multi-channel input features into a trained three-dimensional geometric reconstruction model to obtain a dual-channel output tensor containing a dense depth map of the target object and a corresponding uncertainty map; converting the dual-channel output tensor comprising the dense depth map of the target object and the corresponding uncertainty map into a three-dimensional point cloud under world coordinates; and post-processing the three-dimensional point cloud to obtain a three-dimensional image of the target object.

The invention provides a GNSS-RTK-based topographic mapping method and system, and the method comprises the steps: obtaining multi-source positioning signal data in a target region, the multi-source positioning signal data comprising a satellite original observation value, a receiver antenna phase center deviation parameter, and real-time dynamic differential correction information; performing multi-path interference suppression processing on the multi-source positioning signal data to generate a phase observation sequence after interference suppression; inputting the phase observation sequence after interference suppression into a topographic feature calculation model, and extracting topographic elevation change feature parameters of the target area; performing spatial fusion processing based on the terrain elevation change characteristic parameters and reference elevation parameters in a preset terrain database to generate three-dimensional terrain curved surface data of the target area; and dynamically adjusting the topographic map updating frequency according to the difference degree between the three-dimensional topographic surface data and the historical topographic surveying and mapping result, and outputting a real-time topographic surveying and mapping result. According to the invention, precision, real-time performance and resource efficiency can be considered in complex terrain environment application.

The invention belongs to the technical field of three-dimensional measurement, and discloses a 3D laser line scanning system based on a point cloud positioning algorithm and a control method. The method comprises the following steps: acquiring environment initial point cloud data and scanning target information; carrying out feature extraction, constructing an environment feature description model, carrying out three-dimensional space partitioning processing, carrying out regional reflectivity feature analysis, and constructing a space reflection feature model; adaptive optimization configuration is carried out on the laser scanning parameters, and a scanning strategy is generated; scanning path dynamic planning is carried out on the scanning target information, scanning density distribution calculation is carried out, and a scanning path is generated; performing laser line scanning control by using the scanning path and the scanning strategy, and performing scanning data quality real-time monitoring to obtain scanning quality evaluation data; scanning parameter iterative optimization is carried out on the scanning quality evaluation data, point cloud data real-time fusion reconstruction is carried out, a high-precision three-dimensional model is generated, the modeling precision is improved, and meanwhile the system resource utilization efficiency is optimized.

The invention belongs to the technical field of topographic mapping, and discloses a topographic mapping system and method based on an unmanned aerial vehicle, and the method comprises the steps: a data collection module collects map geological data; the route planning module plans an optimal driving route; the data acquisition module acquires topographic images and point cloud data; the environment parameter monitoring module monitors flight environment parameters in real time; the image data analysis module is used for preprocessing and analyzing the image data; the radar data analysis module preprocesses and analyzes the radar data; the feature extraction and matching module performs feature extraction and matching on the image and the point cloud data; the terrain model construction module constructs a terrain three-dimensional model; and the terrain analysis module performs quantitative analysis on the terrain. According to the method, key feature extraction is performed on images and point cloud data, and image splicing and point cloud registration are realized; the terrain model construction module constructs a three-dimensional terrain model according to feature extraction and matching results, and the terrain analysis module obtains accurate terrain information.

The invention relates to the technical field of fire-fighting early warning, and discloses a fire-fighting early warning system based on image data relevance. The system comprises a multi-source image acquisition module used for acquiring fire-fighting scene multi-source heterogeneous image data; the correlation feature analysis module is used for performing cross-data-source correlation analysis on the data to generate feature vectors; the spatio-temporal dynamic modeling module is used for constructing a multi-dimensional feature fusion space to generate an associated spatio-temporal feature tensor; the resource optimization scheduling library is used for constructing a double-layer collaborative library; and the intelligent early warning control module generates a real-time fire early warning instruction and an emergency response decision through a hierarchical reinforcement learning framework. The system also has the functions of building structure deformation detection, smoke diffusion prediction and the like. Through cooperation of multiple modules, accurate fire-fighting early warning and efficient emergency response are realized, the fire prevention and control capability is improved, and life and property safety is effectively guaranteed.

The invention discloses a wounded rescue life detection and positioning method based on deep learning. The method comprises the steps that S1, multi-view RGB-D images, laser radar point clouds, millimeter wave vital sign radar data and infrared thermal image data are collected and subjected to time synchronization; s2, outputting a pixel-level semantic mask; s3, generating a semantic point cloud of the wounded; s4, forming a metric-semantic double-map data set; s5, generating vital sign scores and writing the vital sign scores into a metric-semantic double-map data set; s6, updating wounded dynamic anchor point attributes in the measurement-semantic double-map data set; and S7, based on the updated metric-semantic double-map data set, calculating the three-dimensional coordinate and rescue priority sequence of each wounded dynamic anchor point, and generating an obstacle avoidance optimized navigation path for avoiding the risk semantic region. The rescue efficiency and safety are effectively improved.

Aspects of the present invention relate to an apparatus for providing driver assistance of a vehicle, an autonomous system, a vehicle, a method, a controller and a non-transitory computer readable medium. The apparatus comprises a first sensor and a second sensor mounted on a vehicle. The first sensor is configured to transmit and receive electromagnetic radiation to detect the presence of an object in a first area, and the second sensor is configured to transmit and receive electromagnetic radiation to detect the presence of an object in a second area. The first area and the second area overlap to define an overlapping area forward of the vehicle. The first area extends from the overlapping area to a first extreme direction having a rearward component and a leftward component, and the second area extends from the overlapping area to a second extreme direction having a rearward component and a rightward component.

The invention belongs to the technical field of unmanned aerial vehicle autonomous navigation, and relates to an intelligent unmanned aerial vehicle navigation method and system based on bimodal obstacle feature extraction. Aligning the information of the multi-modal sensor in time and space through a time-space alignment network; real-time pose information of the unmanned aerial vehicle is obtained through a Kalman filter fused with a positioning failure adaptive strategy; a double-flow obstacle feature extraction network is adopted to extract a composite environment vector containing dynamic obstacle features and static obstacle features from the multi-modal sensor data; training the SAC model based on the maximum entropy by adopting an improved reinforcement learning training method based on curriculum learning to obtain an SAC strategy network; and inputting the composite environment vector and the real-time pose information of the unmanned aerial vehicle into an SAC strategy network, and obtaining the optimal control of the unmanned aerial vehicle in an end-to-end manner, thereby having good path planning performance, and effectively improving the task execution capability and safety of the unmanned aerial vehicle in a complex environment.

The invention discloses a multi-modal information fusion odometer construction method for star catalogue positioning, and relates to the technical field of star catalogue patroller positioning. The method comprises the following steps: carrying out space joint calibration on a monocular camera, a laser radar and an inertial measurement unit, reconstructing a laser radar point cloud by using a timestamp of a camera image, and realizing time synchronization of the camera image and the laser radar point cloud; establishing an IMU pre-integration error model; and performing motion compensation distortion removal on the laser point cloud by using an IMU pre-integration result, and extracting geometric features of the distorted laser point cloud based on a neighbor region smoothness calculation method of a fixed measurement distance. By researching a multimodal information fusion odometer method, the accumulative error of motion measurement is reduced, the positioning precision and stability are improved, technical support is provided for design and development of a star catalogue navigation system, and the problems that a single-modal star catalogue positioning method is weak in environment adaptive capacity and poor in algorithm generalization are solved.

The invention provides a working method of a blind guiding robot based on a laser radar and a camera, and belongs to the technical field of mobile service robots. Accurate space structure information is provided through three-dimensional point cloud data, dynamic traffic characteristics such as traffic lights and zebra crossings are captured through visual images, and comprehensiveness of environmental perception is achieved through complementation of the space structure information and the visual images; through combination of environment information and traffic characteristics, passable areas and barrier distribution in a map are dynamically adjusted, dynamic changes such as traffic light switching and pedestrian movement are adapted, and a navigation route can be updated in real time; the characteristics of traffic light states, zebra crossing and the like are fused into path planning, it is ensured that the robot complies with traffic rules, real-time position and speed constraint, dynamic adjustment of motion trails and efficient obstacle avoidance can be carried out on the robot, and the robot can make a decision and optimize a path autonomously according to human social rules.