1583 results about "Camera image" patented technology

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 cradle head tracking method and device based on a binocular camera and a storage medium, and relates to the technical field of computer vision, and the method comprises the steps: processing image data based on a binocular parallax principle, and generating a three-dimensional coordinate of a center point of a tracking target; based on the three-dimensional coordinates of the camera coordinate system and the offset from the optical center of the camera to the rotation center of the holder, generating three-dimensional holder coordinates through coordinate transformation solution; determining a historical track based on the tracking target feature information and a historical target feature matching result, and outputting an identifier and a three-dimensional position observation value through correlation verification of a three-dimensional holder coordinate and the historical track; inputting an observation updating equation correction state through the identifier and the three-dimensional position observation value, and outputting a three-dimensional prediction position; based on the three-dimensional prediction position and a deviation formula, calculating the angle deviation with the camera image center under the holder coordinate system, and driving the holder to center the target in the picture center according to the angle deviation. The problem that the target tracking effect is poor is solved, and the robustness of target tracking in a complex scene is improved.

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.

Methods and processors for rendering a 3D object are disclosed. The method includes acquiring multi-camera image input including first image frames of the 3D object generated by a first camera and second image frames of the 3D object generated by a second camera, acquiring an initial 3D Gaussian Splatting (3DGS) model having a plurality of initial parameters including an initial frame-wise GS parameter and an initial camera-wise GS parameter, generating an adjusted 3DGS model by adjusting, based on the multi-camera image input, at least one of: the initial frame-wise GS parameter, the initial camera-wise GS parameter, generating, by the adjusted 3DGS model, a 3DGS output and rendering a 2D image of the 3D object using the 3DGS output.

The invention belongs to the technical field of data processing, and particularly discloses a dynamic path planning method and system for an inspection robot, and the method comprises the steps: collecting field data through a sensor, processing a laser scanning point cloud and a camera image through a positioning mapping algorithm, and fusing multi-source information to generate a real-time map. Obtaining a three-dimensional environment model containing a dynamic obstacle position; according to the three-dimensional environment model, obstacle features are extracted, a deep learning model is adopted to identify moving personnel and randomly placed goods, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; according to the invention, the problem of inaccurate dynamic obstacle identification and obstacle avoidance in a complex scene in the prior art is solved.

The invention relates to the technical field of security and protection management, in particular to a campus security and protection management system based on deep learning, which improves the accuracy and robustness of identity recognition by acquiring access control card numbers, face images or fingerprint features and generating standardized identity authentication data. And on the basis of a comparison result of the identity authentication data and the campus database, a behavior chain initialization identifier is generated, and accurate identity binding of the school entering personnel is realized. Furthermore, by collecting multi-camera image stream data, pedestrian re-identification and similarity calculation are executed by using a deep feature matching network, and a cross-camera continuous trajectory data set is generated. And matching the behavior track data set with the conventional path template to generate a behavior offset feature vector. And carrying out joint modeling on the behavior offset characteristics and the identity information through a graph neural network model containing an attention mechanism, and outputting a behavior purpose label and a risk grade score. And a graded security response instruction is generated based on the risk score, so that the missing report rate and the false report rate are effectively reduced.

The invention provides a high-altitude operation risk early warning method and system based on camera image recognition, and relates to the technical field of computer vision, and the method comprises the steps: firstly collecting a video image sequence of a high-altitude operation scene, and generating a fusion feature map containing environment and operation main body features through multi-level feature extraction; performing spatial dimension segmentation and regional feature comparative analysis on the fusion feature map to obtain a spatial risk distribution map containing risk region identification information, processing the spatial risk distribution map of continuous frames based on a time sequence feature fusion rule to generate a dynamic risk evolution map, and calling a risk decision model to perform mode recognition to obtain a dynamic risk evolution map; and generating a risk level classification result and a risk position coordinate set according to the risk level classification result and the risk position coordinate set, and finally generating a risk early warning signal and sending the risk early warning signal to the monitoring terminal, thereby comprehensively, accurately and dynamically monitoring the high-altitude operation risk, and improving the accuracy and timeliness of risk early warning.

The invention relates to the technical field of autonomous navigation of inspection robots, and provides a field inspection robot autonomous navigation and path planning method based on multi-modal sensing. The method comprises the following steps: collecting a laser radar point cloud, a camera image, inertial measurement and positioning data, obtaining an environment semantic feature set through spatio-temporal feature fusion, and generating a semantic occupation map; inputting the current position of the robot, the target point and the semantic occupation map into a trajectory generation network to obtain candidate trajectories meeting obstacle avoidance and path smoothness constraints, and completing task sorting and trajectory splicing in combination with inspection task points to form a global path; in the operation process, the reinforcement learning control model adjusts the linear speed and the angular speed in real time, and path tracking and dynamic obstacle avoidance are achieved. The navigation precision and the operation safety of the inspection robot in the complex field area are improved.

The embodiment of the invention provides a multi-camera video fusion method and system for a robot with a body and a storage medium, and belongs to the field of image processing. The method comprises the following steps: performing time and space alignment on images based on a vision and inertia alignment model to generate a unified aligned image sequence; extracting multi-scale image features based on the uniformly aligned image sequence, and constructing a cross-view residual image for guiding attention fusion operation to generate a fusion image; performing privacy area identification on the fused image, and performing shielding processing on a privacy area to obtain a privacy protection image; and performing semantic compression on the privacy protection image, adjusting an image frame scheduling sequence according to network link state information, and outputting a video signal for controlling a decision. The method is oriented to whole-process optimization of space-time alignment, cross-view fusion and privacy protection processing in the multi-shot image acquisition process of the robot with the body, and video signals for control are output in a self-adaptive mode based on the link state.

The invention provides a multi-modal dynamic cooperative unmanned ship cluster autonomous obstacle avoidance method, which comprises the following steps: firstly, synchronizing LiDAR point cloud, camera images, radar data and AIS data through an IEEE 1588 protocol, and realizing cross-modal space alignment based on an external parameter matrix; secondly, a cross-modal attention mechanism and deformable convolution are combined to establish correlation between a channel and spatial dimensions, so that fused multi-modal features generate high-precision environmental semantic information; then, dynamically dividing unmanned ship task groups based on time-varying graph incremental modularity, and only updating a disturbed edge set to reduce calculation complexity; and finally, the unmanned ship leader optimizes a multi-target path by adopting an improved speed obstacle model (IVO-DWA), and the follower corrects the trajectory through a repulsive force potential field, so that real-time, safe and compliant cluster obstacle avoidance is realized.

The invention provides a camera external parameter calibration method and device based on image and point cloud matching, and the technical scheme of the invention is that a point cloud rendering view corresponding to an original point cloud is rotated, so that the point cloud rendering view and a camera image are superposed visually, and initial space association is established; and directly learning and solving accurate camera external parameters from the local relevance between the original point cloud intensity information and the image RGB information in an end-to-end manner by using a deep learning model. According to the scheme, the strict camera-radar orientation consistency requirement in a traditional method is not needed through rough matching, manual participation in the whole process of traditional calibration is replaced, transition from visual alignment to geometric alignment is achieved, and the calibration efficiency and scene adaptability are remarkably improved. Meanwhile, the scheme of the invention initiates a process of visual field cone cutting-perspective projection rasterization-intensity-RGB end-to-end matching, and compared with a traditional feature point method, the calculation complexity is greatly reduced, and the memory occupation is reduced by 80%.

The invention relates to the technical field of cabin loading and unloading automation, and provides a multi-sensor fusion cabin loading and unloading equipment cooperative positioning method and system. A laser radar is used for scanning a cabin, laser radar point cloud data are generated, and a laser radar coordinate system and a point cloud map are constructed; a monocular camera is adopted to obtain an operation area image of loading and unloading equipment, camera image data is generated, and a camera coordinate system is constructed; performing anti-interference processing on the laser radar point cloud data; performing illumination change influence resistance processing on the camera image data; fusing the laser radar point cloud data with the camera image data to obtain fused positioning information; calculating the distance and angle of loading and unloading equipment according to the fused positioning information, generating a moving instruction, and driving the loading and unloading equipment to perform cooperative positioning; a dynamic object in a cabin is detected through a laser radar and a monocular camera, multi-modal verification is carried out on a detection result, the dynamic object is filtered, and a point cloud map is updated in real time. The problems of insufficient positioning precision, GPS failure and large dynamic environment interference of a single sensor are solved.

The disclosed systems and techniques facilitate efficient detection and navigation of reduced drivability areas in driving environments. The disclosed techniques include, obtaining, using a sensing system of a vehicle, a set of camera images, a set of radar images, and / or a set of lidar images of an environment. The techniques further include generating, using a first neural network (NN), camera feature(s) characterizing the camera images, generating, using a second NN, radar features characterizing the radar images, and / or generating, using a third NN, lidar feature(s) characterizing the lidar images. The techniques further include processing the camera feature(s), the radar feature(s), and the lidar feature(s) to obtain an indication of a reduced drivability area in the environment.

The invention relates to the technical field of computer vision, image recognition and intelligent medicine management, in particular to a medicine bottle label content recognition method based on multiple cameras and YOLOv8. The method at least comprises the following steps: S1, deploying a medicine bottle label generation system, and generating a label; s2, multi-camera image acquisition and preprocessing; s3, carrying out chessboard calibration and space positioning; s4, medicine bottle label detection and label character recognition and structured analysis; and S5, system integration and application. According to the invention, through combination of multi-camera and multi-angle acquisition and checkerboard calibration positioning and combination with YOLOv8 label detection and OCR identification, high precision, high efficiency, end-to-end automation and system integration of medicine bottle label identification are realized, the defects of precision, efficiency, environmental adaptability and management integration in the prior art are overcome, and the system is suitable for popularization and application. The method has obvious technical advantages and practical value.

The invention relates to the technical field of vision assistance, and discloses an anesthesia puncture positioning method and system based on vision assistance, and the method comprises the steps: accurately positioning an anesthesia puncture point through multi-view image fusion, hyperspectral image processing, image preprocessing, illumination equalization, edge enhancement, depth feature extraction and the like. The method comprises the following steps: firstly, constructing an image coordinate system, collecting a plurality of camera images, and obtaining a fused clear image through a designed multi-view fusion algorithm and distance calculation; and in combination with a hyperspectral image fusion algorithm, the image quality is further improved. Then, residual mapping filtering denoising and local histogram enhancement are used for illumination equalization, and the image contrast and edge details are enhanced; a convolutional neural network is adopted, interest point detection is carried out, a Hessian matrix is utilized to describe image second-order changes, and local depth features are extracted. And accurate positioning of an anesthesia puncture point is realized through a weighted soft voting classifier and a dynamic threshold method.

The invention belongs to the technical field of laser radar-camera panoramic segmentation, and particularly relates to a multi-mode panoramic segmentation method for multi-view space-time alignment and implicit feature interaction, and the method is executed by a multi-mode panoramic segmentation network, and comprises the steps: S1, obtaining laser radar point cloud data and multi-view camera image data in the same scene; s2, performing double-branch feature coding on the laser radar point cloud data; performing multi-scale image feature extraction on the camera image data; s3, generating false point cloud features with geometric perception capability; s4, generating semantic pixel features; s5, performing implicit fusion on the pseudo point cloud features generated in the S3 and the semantic pixel features generated in the S4 to obtain cross-modal fusion features; and S6, based on the cross-modal fusion features obtained in the S5, generating a unified panoramic segmentation result containing semantic tags and instance IDs. The method can effectively improve the robustness and precision of multi-mode panoramic segmentation in a complex urban environment.

The invention discloses a submarine topography reconstruction system and method based on optical mobile measuring and scanning, and relates to the technical field of ocean exploration, and the system comprises the following units: an image collection unit, which is used for coordinating the emission of 532nm pulse laser and the collection of a gating camera image through a synchronous sequential circuit, a triangulation measurement optical structure with separated emission and reception is adopted, and a submersible can be carried for mobile measurement and scanning; the system calibration unit is used for carrying out joint calibration on camera parameters and a laser plane so as to determine system calibration parameters, so that the spatial position of a laser stripe accurately corresponds to the actual coordinate of the target surface; and the image processing unit is used for preprocessing the image acquired by the system and further extracting the coordinates of the center line of the laser stripe in the preprocessed image. According to the invention, adjacent splicing can be carried out through continuously collected laser stripe images to construct two-dimensional submarine landform information, and three-dimensional reconstruction of submarine topography can be carried out by using generated point cloud data.

The invention provides a roadway surface and internal defect synchronous sensing method based on point cloud registration. The roadway surface and internal defect synchronous sensing method comprises the following steps: S1, multi-sensor joint calibration; s2, synchronously acquiring data; s3, data preprocessing; s4, projecting a camera image to the point cloud; s5, surface defect identification: S5.1, geometric defect identification based on point cloud; s5.2, performing visual defect identification based on the image, and marking an identification result in the point cloud; s6, integrating the geometric defect point cloud and the visual defect point cloud; s7, identifying internal defects; s8, determining an internal confirmation three-dimensional position; s9, marking an internal defect point cloud; and S10, uniformly visualizing the surface defects and the internal defects. According to the invention, through cooperative work of the laser radar, the ground penetrating radar and the camera, the same perception of tunnel defect surface and internal defects is realized through point cloud visualization.

A camera system has at least a first and a second camera each with an image sensor. Active areas of the image sensors of the first and second camera are determined and define an extended image space of a real-time panoramic video. A bounding box captures a fixed position in space or an object is set. The bounding box moves through extended image space. Image data determined only by the bounding box is harvested from camera image sensors and displayed within a window on a screen in real-time. Scan-line control of images sensors based on the bounding box is updated in real-time to form an e-gimbal. Steps of the e-gimbal are performed by a machine learning inference phase on a processor.

The invention discloses a geological vision large model-based drilling camera intelligent interpretation method, which comprises the following steps of: 1) acquiring and preprocessing a drilling image, and constructing a sample library containing geological labels; 2) based on the existing general visual large model, embedding a geological feature attention module and a lithology classification adapter, performing special optimization in combination with deep coal mine geological features, and constructing a geological visual large model; 3) constructing a geological algorithm dictionary to convert geology knowledge experience into a computable algorithm module, and embedding the algorithm module into a geological vision large model; and 4) realizing sample adaptive diagnosis and repeated learning through an AI module. According to the method, the geologic vision large model is constructed, the geologic feature attention module and the geologic algorithm dictionary are embedded in the model, and a sample adaptive diagnosis and repeated learning mechanism is fused, so that the accuracy of geologic feature recognition in the borehole camera image can be remarkably improved, and efficient and accurate interpretation of the borehole image is realized; and accurate geological data support is provided for underground operation.

The invention relates to the technical field of image quality optimization, and discloses a hunting camera imaging quality optimization method based on multimode data fusion. The method comprises the following steps: acquiring a visible light image, a thermal infrared image, a laser ranging point cloud and inertial measurement unit data, and carrying out space-time alignment and registration to form synchronous multimode data. A controllable imaging parameter set and scene context description information are separated by performing joint feature extraction on synchronous data. And for each parameter to be optimized, the system retrieves a plurality of candidate strategies from the imaging optimization knowledge base by taking the current value and the scene context as query conditions, selects an optimal strategy through fusion decision calculation, and finally generates and executes a global imaging parameter optimization instruction set to control a camera to complete image capture. According to the method, intelligent optimization of imaging parameters based on depth scene understanding is realized, and the image quality and adaptability of the hunting camera in a complex environment are improved.

The invention relates to an external parameter adjusting method and device of a vehicle camera and vehicle equipment. The method comprises the following steps: reading vehicle landing data, and extracting three-dimensional target detection data and camera image data of a static or low-speed moving road environment target from the vehicle landing data; obtaining target pixel coordinates of a road environment target in the camera image data; constructing a coordinate index structure according to the target pixel coordinates; determining a plurality of candidate camera external parameters and two-dimensional projection coordinates of the candidate camera external parameters to the sampling points of the plurality of three-dimensional target detection data; querying the coordinate index structure, and determining a coordinate index result of the two-dimensional projection coordinates; and screening target camera external parameters of the vehicle camera from the plurality of candidate camera external parameters according to a projection error obtained by a coordinate index result. Therefore, statistical optimization can be carried out by using mass driving data, so that the camera external parameters with high reliability can be obtained by adjusting and calibrating without depending on a specific calibration scene.

To suppress an unintended situation for a user regarding handling of a camera image.SOLUTION: An information processing system includes: a communication control section for controlling data communication among multiple terminals including a first terminal and a second terminal that belong to the same group; a camera image acquisition section for acquiring a camera image of a camera corresponding to either terminal that belongs to the same group; a restriction setting section for setting restriction of display of a camera image of a camera corresponding to a terminal other than at least the second terminal in the second terminal according to a user's operation; a game application execution section for executing game processing according to a user's operation in the first terminal and outputting a first game image and first information; and a display mode determination section for determining a display mode of a first game image in the second terminal on the basis of the setting of restriction of display and first information.SELECTED DRAWING: Figure 1

The invention discloses an electric arc three-dimensional reconstruction system and method based on binocular vision. The system comprises a double-camera image acquisition module, a correction module, a boundary contour point extraction module and a three-dimensional reconstruction module. The dual-camera image acquisition module completes dual-camera parameter calibration and dual-vision arc image shooting operation at different positions; the correction module corrects an optical distortion area and a breakpoint area of the double-vision arc image to obtain a corrected double-vision arc image; the boundary contour point extraction module adopts a third-order Bezier curve to perform segmentation fitting on the boundary contour of the corrected double-vision arc image to obtain continuous double-vision arc boundary points; and the three-dimensional reconstruction module reconstructs a three-dimensional contour line of the electric arc through a triangulation method according to the camera calibration parameters and the double-vision electric arc boundary points, and generates a corresponding three-dimensional point cloud data set. According to the method, the distortion and breakpoint phenomena of the arc image can be repaired, and high-precision reconstruction of three-dimensional data of the arc is realized.

The invention discloses a million-frame-level industrial vision system and method based on event driving and compressed sensing, and the system is characterized in that an event camera imaging module in the system captures the brightness change of each pixel in a field of view of the event camera imaging module in an asynchronous manner, and generates an event containing a pixel coordinate, a timestamp and change polarity for each change; a compressed sensing coding module constructs sparse image vectors for events in a time window, and the sparse image vectors are projected to low-dimensional observation vectors through an observation matrix phi; the sparse image reconstruction module is used for optimizing an objective function through sparse constraint and total variation regularization; a dynamic ROI compression module controls a compression mask function according to the event density and a gradient threshold. The method can break through the limitation of the traditional frame rate, has the advantages of high precision, high efficiency, low power consumption, strong robustness and the like, and has a wide industrial application prospect.

The invention discloses an operation interaction method and system applied to camera image editing, and relates to the technical field of image processing, and the method comprises the steps: obtaining a voice semantic heat map, a pointing intensity map, a touch confidence map and a gazing confidence map based on a multi-modal interaction data packet, and calculating an image feature matrix at the same time; fusing into a multi-modal evidence graph through a normalized scale; performing semantic segmentation according to the image feature matrix to obtain a semantic segmentation first draft and a pixel-by-pixel category confidence coefficient, and performing position correlation weighting on the pixel-by-pixel category confidence coefficient by taking the multi-modal evidence graph as a confidence coefficient modulation factor to generate a candidate object mask sequence; and performing highlight display on the candidate object mask sequence, and performing conflict resolution and priority rearrangement in combination with the multi-mode evidence graph to generate a target object mask. According to the method, deep fusion of the interaction intention and image segmentation is realized, the precision and consistency of candidate region detection are improved, and the stability of real-time rendering and the reliability of an editing result are improved.

The invention discloses a monitoring camera image anomaly detection method and system, and relates to the technical field of intelligent monitoring, and the method comprises the steps: obtaining an original video stream, extracting a pixel matrix of a current frame, carrying out the joint coding of the pixel matrix and a motion vector field of an adjacent frame into a quantum state bit vector, inputting the quantum state bit vector into a pre-constructed anomaly detection operator, and carrying out the detection of the anomaly. Executing quantum state evolution calculation in the Hilbert space, and outputting a two-dimensional distribution matrix of each pixel region; integrating the marked binary image and the physical verification conclusion, and when the quantum anomaly probability value reaches the alarm standard and passes the physical verification, outputting an anomaly alarm signal; and the abnormal alarm signal is converted into a confrontation training sample, a cross-scene virtual sample is generated through quantum noise injection, and an abnormal detection operator is dynamically updated. According to the method, the Hilbert space quantum state evolution step is combined, unitary transformation is applied to the quantum state bit vector, the orthogonal projection operation is executed, the accurately quantified abnormal probability distribution matrix is generated, and accurate assessment of the abnormal risk is achieved.

The invention provides a camera calibration method and device, electronic equipment and a storage medium, and belongs to the technical field of data processing, and the method comprises the steps: obtaining laser radar point cloud data, inertial measurement unit data and reference camera image data of a vehicle, and obtaining initial calibration parameters of a to-be-calibrated camera; based on the laser radar point cloud data, the inertial measurement unit data and the reference camera image data, constructing a point cloud map containing color textures and a pose track of a vehicle, and converting the point cloud map into a surface grid model; according to the pose track and the initial calibration parameters, the surface grid model is rendered to a visual angle of the to-be-calibrated camera, and a rendered image and a rendered depth map corresponding to the to-be-calibrated camera are generated; based on the real image data, the rendered image and the rendered depth map, establishing a corresponding relationship between two-dimensional image features and three-dimensional space points in the real image data; and constructing a re-projection error objective function based on the corresponding relationship, and optimizing the external reference and the internal reference of the camera to be calibrated by minimizing the objective function.

The invention discloses an unmanned aerial vehicle high-precision construction lofting method and system based on an RTK / PPK technology, and belongs to the technical field of construction lofting, and the method comprises the steps: based on a LiDAR point cloud, an IMU attitude and a camera image of timestamp alignment, realizing space-time registration through extended Kalman filtering or factor graph optimization, and generating a high-precision construction area three-dimensional model; based on the deviation thermodynamic diagram and the text report and after adjustment of construction personnel, the unmanned aerial vehicle performs automatic reinspection until the verification deviation of comparison of two times of actual measurement data is within a convergence threshold value; and static baseline calculation is performed on PPK original data stored by the unmanned aerial vehicle, and accurate positioning data of the unmanned aerial vehicle in a signal interruption period is calculated in combination with synchronous observation data of a base station, so that full-process automatic high-precision lofting in a complex environment is realized.

The invention belongs to the technical field of data processing, and particularly discloses an intelligent path planning and control system for an automatic driving carrying robot, and the system is operated through the following method: collecting environment data through a multi-mode sensor, and processing a laser radar point cloud and a camera image; fusing multi-source information to generate a real-time map, and obtaining a three-dimensional environment model containing dynamic obstacle positions; according to the three-dimensional environment model, obstacle features are extracted, an improved neural network model is adopted to identify a plurality of current obstacles, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; the objective of the invention is to solve the problem that accurate sensing, real-time decision making and efficient obstacle avoidance are difficult to realize in a complex dynamic environment in the prior art.