21945 results about "Lidar" 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.

A modeling method for three-dimensional real-time measurement reconstruction, comprising: on the basis of a three-dimensional camera, a laser radar, and an infrared sensor device, generating a multi-source original data set; performing data preprocessing; performing feature extraction and data fusion; performing three-dimensional space construction; by means of a deep machine learning algorithm, performing real-time dynamic adjustment and optimization on a model; and, by adopting an advanced image processing algorithm, generating a high-precision three-dimensional model.

The invention provides a real scene three-dimensional modeling method and system fusing laser point cloud and images, and belongs to the technical field of urban three-dimensional modeling. According to the method, laser point cloud data and multi-view image data of a target scene are acquired through a synchronous acquisition device, and joint calibration is performed on a laser radar and a camera to generate space-time synchronization parameters. And a dynamic weight registration algorithm is utilized to dynamically adjust the registration weight according to the point cloud density distribution and the image texture complexity so as to realize the multi-level registration of the laser point cloud and the image data. The registered data is used for constructing a three-dimensional geometric model and generating a seamless texture map, meanwhile, semantic-driven hole repairing is carried out on a model missing area, and finally a complete three-dimensional model is output. According to the real-scene three-dimensional modeling method and system fusing the laser point cloud and the image, the limitation of single data source modeling is solved, and real-scene three-dimensional modeling with high precision, high integrity and real-time updating is realized.

A LIDAR-to-LIDAR alignment system includes a memory and an autonomous driving module. The memory stores first and second points based on outputs of first and second LIDAR sensors. The autonomous driving module performs a validation process to determine whether alignment of the LIDAR sensors satisfy an alignment condition. The validation process includes: aggregating the first and second points in a vehicle coordinate system to provide aggregated LIDAR points; based on the aggregated LIDAR points, performing (i) a first method including determining pitch and roll differences between the first and second LIDAR sensors, (ii) a second method including determining a yaw difference between the first and second LIDAR sensors, or (iii) point cloud registration to determine rotation and translation differences between the first and second LIDAR sensors; and based on results of the first method, the second method or the point cloud registration, determining whether the alignment condition is satisfied.

A LIDAR system has a semiconductor chip configured to concurrently output multiple LIDAR output signals. The semiconductor chip includes alternate waveguides. Each of the alternate waveguides carries a different outgoing LIDAR signal. Each of the LIDAR output signals includes light from a different one of the LIDAR output signals. The semiconductor chip includes a reflecting surface that receives incoming LIDAR signals that each includes light from a different one of the LIDAR output signals. The semiconductor chip also includes comparative waveguides. Each of the comparative waveguides receives a comparative signal from the reflecting surface. Each of the comparative signals includes light from a different one of the incoming LIDAR signals.

The present invention provides a three-dimensional reconstruction method integrating a laser radar and oblique photography, comprising: S1, acquiring an oblique image and image data and preprocessing same to obtain a first oblique image, wherein the image data comprises POS data; S2, acquiring coordinates of a ground control point and high-precision POS data; S3, correcting the oblique image to obtain a second oblique image; S4, performing aerotriangulation on the second oblique image, and obtaining an effective matching point pair by means of improved SIFT; S5, performing combined adjustment on the basis of the effective matching point pair and performing dense matching to obtain a dense point cloud; S6, acquiring a laser point cloud and preprocessing same; S7, unifying the coordinates of the laser point cloud and the dense point cloud, and using improved ICP to obtain a fused point cloud; and S8, constructing a triangulated irregular network and performing texture mapping to generate a three-dimensional model. In the present invention, on the basis of the advantages of high resolution and strong anti-interference capability of a laser radar, defects in an oblique photography three-dimensional model are mitigated, and meanwhile, an oblique image is corrected and an algorithm used in feature matching is improved, thereby improving the precision, integrity and efficiency of the three-dimensional model.

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 relates to the technical field of navigation, and particularly provides a quadruped robot autonomous navigation method and system for a special environment, and the method comprises the steps: generating a semantic tag for depth point cloud data corresponding to a depth image through employing a recognition result of a target detection network for the depth image; calculating local odometer information according to the laser point cloud data and the inertial sensor data by using a laser radar mileage calculation method; fusing the depth point cloud data with the semantic label, the laser point cloud data with the semantic label and the local speedometer information to obtain positioning data in a pre-constructed point cloud map; constructing a multi-layer cost map according to the pre-constructed point cloud map, the newly acquired depth image, the laser point cloud data and the inertial sensor data by using a sensing method of a region planner; and generating navigation path information according to the positioning data and the multi-layer cost map by using a global path planning algorithm and a local path planning algorithm. The method improves the navigation precision.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle autonomous obstacle avoidance decision-making method and system based on multi-source sensor fusion, and the method comprises the steps: achieving the time-space synchronization of a laser radar, a visual camera and a millimeter-wave radar through timestamp alignment and coordinate mapping, and constructing a dynamic obstacle grid map; fusing multi-source data based on a dynamic Bayesian network, and dynamically adjusting the confidence coefficient weight of the sensor in combination with the environment illumination intensity and the barrier surface material; and adopting a reinforcement learning model to generate an incremental obstacle avoidance strategy, and triggering a grading response instruction according to the risk assessment grade. The problem of fusion errors caused by spatial-temporal asynchronization of multi-source sensor data is solved, and the real-time obstacle avoidance success rate of dynamic obstacles is increased.

The invention discloses an obstacle avoidance method and device for unmanned aerial vehicle power inspection autonomous flight based on a laser radar and a storage medium, and relates to the technical field of unmanned aerial vehicle obstacle avoidance, and the method comprises the steps: configuring the laser radar and a visual sensor on an unmanned aerial vehicle, and collecting the three-dimensional point cloud data and image data of a flight environment in real time; optimizing the point cloud data to generate a three-dimensional environment model; and based on the three-dimensional environment model, classifying the obstacles into static obstacles and dynamic obstacles, if the obstacles are static obstacles, identifying the types of the obstacles through a CNN model and performing positioning, and if the obstacles are dynamic obstacles, clustering motion tracks through a K-Medoids clustering method. According to the invention, static obstacles can be identified and positioned, and motion trail prediction can be carried out on dynamic obstacles such as flying birds through a dynamic obstacle prediction technology, so that a preliminary flight path is generated, surrounding obstacles are sensed and avoided in real time in the flight process, and it is ensured that the unmanned aerial vehicle can be smoothly executed in an electric power inspection task.

The invention discloses an intelligent driving multi-sensor fusion data processing system, which belongs to the technical field of fusion data processing and comprises a data preprocessing unit, a data fusion unit and a data post-processing unit. The data preprocessing unit normalizes the original data of the laser radar, the camera, the millimeter wave radar and the ultrasonic sensor through a standardization, cleaning, noise reduction and calibration synchronization module; the feature level fusion module deeply digs the characteristics of each sensor, and fuses geometric, visual, motion and close-range obstacle features by using a deep neural network; the decision-making level fusion module generates a driving instruction through weighted voting and fuzzy logic reasoning through classification and situation evaluation; and the data post-processing unit combines the vehicle and road condition optimization instruction, evaluates the risk, and stores the data feedback optimization system. The system can improve the sensing precision and decision reliability, enhances the expansion adaptability of the system, and provides guarantee for safe and efficient operation of intelligent driving.

The invention relates to the technical field of unmanned vehicle control, and discloses a smart city environmental sanitation unmanned vehicle path planning and real-time monitoring method. The method comprises the following steps: firstly, acquiring a multi-source environment sensing data set such as laser radar point cloud data, a camera image sequence and real-time traffic flow information; local road network features are extracted based on laser radar point cloud data, a dynamic target motion prediction map is generated according to a camera image sequence, and real-time traffic flow information is processed to generate a regional traffic efficiency evolution curve. And inputting the data into a path optimization model to generate an initial path sequence, dividing cleaning task priorities, fusing related data and generating a final path planning scheme through a reinforcement learning algorithm. In addition, operation state data of the unmanned vehicle are collected in real time, and a path correction instruction set is generated through an anomaly detection model to update the strategy network. According to the method, the rationality and the operation efficiency of the path planning of the environmental sanitation unmanned vehicle can be improved, real-time monitoring is realized, and the operation safety and the management intelligence level are enhanced.

An autonomous robotic vehicle with dynamic path planning capabilities, including: a dynamic path planning module configured to generate and adapt navigation paths in real time based on environmental changes and mission objectives; a sensor fusion module that integrates data from multiple sensors, including LiDAR, radar, cameras, ultrasonic sensors and GPS, to create a comprehensive environmental model; an obstacle avoidance module capable of detecting and predicting both stationary and moving obstacles and modifying the navigation path to avoid collisions; an artificial intelligence-based decision-making module for analyzing environmental data and learning from past navigation scenarios for improved performance; a decentralized control module that enables the independent operation of the vehicle in areas with interrupted or no connection to a central server; an energy management module configured to optimize battery usage by dynamically adjusting operating parameters based on real-time data and mission requirements; a cooperation module that enables communication and coordination between multiple autonomous robot vehicles for fleet management or the synchronized execution of tasks; a fail-safe module to ensure uninterrupted operation and data security in the event of communication failures or hardware malfunctions.

An aerial vehicle configured for operation within indoor spaces has a meshed construction with a housing defined by upper and lower sections having meshes provided above and below propellers and motors of the aerial vehicle. The aerial vehicle also includes a suite of sensors such as LIDAR sensors, time-of-flight sensors, cameras, ultrasonic sensors, or others. Meshes of the upper and lower sections include central openings along with spokes and concentric rings provided about the central openings. Meshes of the lower section have substantially larger central openings than meshes of the upper section, but feature more dense spokes or concentric rings beneath tips of the rotating propellers, which may be hinged or foldable in nature. Data captured by sensors of the aerial vehicle may be utilized for any purpose, such as to generate environment maps of an indoor space, or to monitor the indoor space for adverse conditions or events.

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 relates to the technical field of rail traffic safety detection, in particular to a railway freight train key part online monitoring system based on an unmanned aerial vehicle, which comprises an unmanned aerial vehicle control module, a multi-modal data acquisition module, an edge calculation module, a central processing module and a feedback execution module. A dynamic three-dimensional grid flight path is generated by fusing a train Beidou positioning signal and a millimeter wave radar sensing result, a system is provided with an infrared thermal imager, a laser radar and a high-speed polarization camera, bearing temperature, train body point cloud and a train coupler image sequence are obtained, and temperature rise area identification, structural deformation extraction and coupling state modeling are completed on the edge side. The central processing module outputs a multi-dimensional safety assessment result, and the feedback module generates a compensation control instruction and a graded early warning signal based on the risk fusion index. The method has the advantages of being high in autonomy degree, high in recognition precision and high in response speed, and is suitable for full-time structural intelligent inspection and early warning of the freight train in a high-speed operation environment.

The present invention relates to the technical field of terrestrial laser scanning-based forestry data processing, and disclosed are a broad-leaved forest individual tree segmentation method and system based on branch and trunk information guidance. The method comprises: taking a terrestrial laser scanning-based broad-leaved forest point cloud as a processing object, and using RANSAC cylinder fitting in combination with growth characteristics of a tree trunk to perform tree trunk detection; extracting, starting from the top of the tree trunk in a low-vegetation area, tree branches and trunk by means of segmented growth of the branches and trunk in combination with changes in the thickness of branch and trunk segments; and segmenting, starting from the tips of the branches, a tree crown leaf point cloud by means of layer-by-layer growth. The present invention achieves a stronger tree trunk detection capability under the conditions of lush low vegetation and complex terrain, and moreover, the present invention can achieve accurate segmentation of tree crowns when large and small tree crowns are intertwined and multiple tree crowns are closely surrounded. The algorithm is highly efficient and easy to use, and has great practical significance for improving the semantic understanding capability of forest scenes, and assisting in forest resource survey, vegetation ecology research, and calibration and verification of satellite remote sensing products.

The invention relates to the technical field of unmanned aerial vehicle flight path planning, in particular to an unmanned aerial vehicle real-time path planning system and method based on dynamic weight distribution and multi-source data fusion. The system comprises a multi-source data fusion module, an integrated laser radar, a millimeter wave radar, a visual sensor and a Beidou positioning unit. The dynamic weight distribution module dynamically adjusts the weight coefficient of each sensor according to the environmental complexity, the threat level and the state of the unmanned aerial vehicle by adopting a mixed decision-making mechanism combining fuzzy logic and reinforcement learning; an improved RRT * algorithm and a Markov decision process are built in the real-time path planning module, and a global optimal path and a local obstacle avoidance track are generated by adopting a layered planning architecture; the unmanned aerial vehicle cooperative control module comprises a dual-redundancy flight control system and a dynamic obstacle avoidance unit; and the communication relay module supports 5G and low-orbit satellite dual-mode communication, updates an environment cognitive model of each unmanned aerial vehicle through federated learning, and realizes multi-source fusion real-time path planning based on dynamic weight distribution and the unmanned aerial vehicles.

The invention relates to the technical field of visual recognition, and discloses a visual recognition obstacle detection method, device and equipment and a storage medium, and the method comprises the steps: carrying out the time alignment and space registration of image data collected by a visual camera in a walking-instead-of-walk robot and point cloud data collected by a laser radar, and obtaining a multi-modal data stream; inputting the multi-modal data stream into a recursive gating deep convolutional neural network for feature extraction to obtain a time sequence multi-modal fusion feature; calculating an inter-modal correlation matrix, and constructing a three-dimensional voxel grid to obtain a three-dimensional environment representation; performing two-stage detection on the three-dimensional environment representation to obtain a comprehensive obstacle representation; environment complexity evaluation is carried out according to the comprehensive obstacle representation, and a self-adaptive real-time obstacle avoidance control strategy is generated; the adaptive real-time obstacle avoidance control strategy is converted into an optimal control problem, and rolling time domain optimization is performed to obtain an optimal obstacle avoidance control sequence, so that the accuracy of obstacle detection is improved.

The invention discloses an object labeling method and system based on laser point cloud, particularly relates to the technical field of laser point cloud processing, and is used for solving the problem of inaccurate labeling result caused by a complex reflection phenomenon caused by a non-Lambertian surface in the prior art. The method comprises the steps of obtaining original point cloud data collected by a laser radar and performing preprocessing; identifying a non-Lambertian reflection region based on the reflection intensity information and the geometric topology feature, and carrying out filtering correction; correcting false mirror image points through multi-layer iteration correction, and reconstructing boundaries to generate corrected point cloud data; grouping and classifying target objects in the corrected point cloud data based on a clustering algorithm combining global features and local features; finely dividing the boundary of the preliminary segmentation result by adopting an attention mechanism network, and evaluating the uncertainty of a boundary region through a local mutual information entropy; and adjusting a boundary division strategy according to an uncertainty evaluation result, and generating an optimized segmentation result.

The present invention provides an intelligent robot for power inspection and an inspection method thereof, comprising a quadruped robot, wherein a laser radar, a voiceprint sensor and an infrared sensor are provided at the front end of the quadruped robot; a pan-tilt base is provided at the middle of the quadruped robot, and a camera is provided on the pan-tilt base; a six-degree-of-freedom mechanical arm is also provided at the tail of the quadruped robot, and a mechanical claw is provided at the end of the mechanical arm. The robot flexibly plans tasks based on the layout of the substation, sets inspection cycles for different equipment according to their importance, and automatically generates accurate task plans, thereby avoiding the tediousness and omissions of manual planning and improving planning. The advanced path planning algorithm enables the robot to quickly plan the optimal path, integrates multiple positioning technologies to ensure accurate navigation, and effectively reduces travel time. Multiple inspection modes are carried out in parallel, non-stop inspections are dynamic screening, routine inspections are comprehensive inspections, and non-preset inspections are flexibly targeted at specific points, which greatly improves the coverage efficiency of the inspection and avoids detection blind spots.

The invention relates to the technical field of tunnel engineering, in particular to a tunnel surrounding rock grading method and system, comprising intelligent sensing and data acquisition, multi-source data fusion and modeling, hybrid model dynamic grading, real-time decision and support optimization, online learning and dynamic feedback, and risk early warning and emergency response. Compared with the prior art that a geological data acquisition mode combining manual drilling coring and low-resolution geophysical prospecting is adopted, efficiency is low, subjective errors are large, and a complex geological structure is difficult to cover, unmanned aerial vehicle LiDAR scanning, intelligent rock core image analysis and a high-density IoT sensor network work cooperatively, and the working efficiency is greatly improved. Real-time dynamic acquisition of full-section geological information is achieved, manual intervention errors are eliminated in combination with a multi-source data fusion algorithm, the automation level and three-dimensional space representation precision of data acquisition are remarkably improved, and a high-resolution holographic data base is provided for surrounding rock classification.

The invention discloses a three-dimensional environment reconstruction optimization method based on multi-sensor fusion data, and relates to the field of three-dimensional environment reconstruction optimization, and the three-dimensional environment reconstruction optimization method based on the multi-sensor fusion data comprises the following steps: S1, collecting multi-source sensor data, and constructing a data set under a unified coordinate system; s2, generating dense visual point cloud, and extracting laser point cloud features to construct a model; s3, establishing a local three-dimensional model, and generating a local environment image; s4, shadow parameters are extracted through shadow geometric analysis, and time sequence optimization is carried out; s5, consistency verification and correction are carried out, and three-dimensional reconstruction data are output; and S6, comparing the reconstruction data with the navigation map database, and carrying out map optimization updating. According to the method, time synchronization and space calibration are carried out on data acquired by the depth camera and the laser radar, complete and accurate three-dimensional information modeling of the target environment is realized, and the geometric precision of environment reconstruction and the image detail reduction capability are improved.

Disclosed in the present invention is a power distribution network point cloud model construction system based on three-dimensional visualization, the system comprising: a data collection module, which collects model data of a power distribution network by means of receiving a signal reflected by a LiDAR scanner; a data denoising module, which performs denoising processing on the collected model data, so as to eliminate a data error caused by noise; a point cloud reconstruction module, which simplifies denoised point cloud data, screening important features in the power distribution network, and constructing a point cloud model using the screened data; a visualization module, which uses a visualization technique to present the constructed model, so as to realize an interactive visualization operation; and an operation state evaluation module, which evaluates the safety performance of the power distribution network on the basis of fault data and a historical operation record of the power distribution network. In this way, the operation condition of the power distribution network can be visually known by means of a three-dimensional visualization interface, and thus the operation and maintenance efficiency is improved, and accurate data support is provided for the management and operation and maintenance of the power distribution network.

The invention relates to an automatic driving vehicle path planning method and device based on a large language model, equipment and a medium. The method comprises the steps that a traffic scene RGB image, a traffic scene depth image, laser radar point cloud data and inertial measurement unit data of an automatic driving vehicle are acquired; performing feature extraction on the traffic scene RGB image, the traffic scene depth image, the laser radar point cloud data and the inertial measurement unit data to construct multi-modal traffic scene information; calling a preset large language model to determine the motion state of the autonomous vehicle and natural language description of traffic targets around the autonomous vehicle according to the multi-mode traffic scene information; and determining the attention weight of the target node by adopting an attention mechanism in a large language model according to the natural language description of the traffic target, and determining the optimal driving path by adopting a preset path planning algorithm according to the attention weight of the target node, the motion state of the autonomous vehicle and the target position. According to the invention, the adaptive capability of path planning is obviously improved.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an inspection unmanned aerial vehicle flight control management method, which comprises the following steps: step 1, an unmanned aerial vehicle obtains environment three-dimensional point cloud data through a laser radar, captures terrain visual information through an RGB camera and a depth camera, obtains real-time position and attitude information of the unmanned aerial vehicle through a GPS and an inertial measurement unit, and obtains the real-time position and attitude information of the unmanned aerial vehicle; and fusing the collected data to construct a spatial grid map, and taking the grid map as input data of path planning. A high-precision space grid map is generated through three-dimensional environment perception and modeling in combination with an unmanned aerial vehicle multi-sensor fusion technology, and a path is updated in real time by using dynamic environment data, and an air route is planned and optimized, so that path dynamic adjustment and energy consumption minimization of the unmanned aerial vehicle in a complex dynamic environment are realized; and the effects of improving the task execution efficiency, reducing the flight energy consumption and improving the flight safety are achieved.

The invention discloses an operation method of a humanoid agricultural robot based on path planning, which relates to the related technical field of agricultural robots and comprises the steps of multi-sensor data acquisition, data processing and map generation, map updating, target crop image recognition and positioning, global path planning, local path obstacle avoidance and picking action execution. The invention further discloses an operation system of the humanoid agricultural robot based on path planning. The operation system comprises a humanoid robot body, a visual sensor, a laser radar, a depth camera, an inertial measurement unit, an auxiliary sensor, an actuator module, a control system, a communication module and a power supply system. The inventor-shaped robot can recognize the positions and shapes of the tools in advance by using a sensitive visual perception system of the inventor-shaped robot, can skillfully cross over or bypass the tools by virtue of the flexibility of the inventor-shaped robot, can accurately determine the positions and distribution conditions of crops in a three-dimensional space, and generates a comprehensive picking environment map.

The invention discloses a multi-sensor fusion positioning mapping and thermal imaging device and a computer readable medium. The equipment integrates positioning mapping sensors such as a thermal imaging camera, a visible light camera, a laser radar and an inertial navigation system, and is specially designed for high-risk and complex fire emergency environments. Through an end-cloud integrated cluster cooperation system and a multi-link stable transmission technology and in combination with intelligent decision support, high-precision real-time positioning and dynamic 3D temperature measurement mapping can be realized in an environment in which satellite navigation signals are weak or unavailable. The multi-mode portability and environmental adaptability design of the system ensures the high efficiency and safety of an operator / unmanned system under extreme conditions, and the advanced data processing and analysis technology of the system significantly improves the accuracy and response speed of rescue operation. Due to the innovative characteristics, the method has remarkable practical application value and market prospect in the field of fire-fighting emergency rescue.

The invention discloses an intelligent photoelectric theodolite aerial target positioning and tracking system, which relates to the technical field of photoelectric detection, and comprises a multi-mode photoelectric sensor module integrating visible light, infrared thermal imaging, a laser radar and a polarized light sensor and supporting spectrum adaptive switching; the dynamic noise suppression processing module is used for eliminating environmental interference based on a time-space domain hybrid filtering algorithm; the multi-target tracking control module adopts a time-sharing partition scanning strategy and a graph neural network data association algorithm; the anti-interference servo driving module is used for realizing stable tracking under strong disturbance through inertial navigation-visual fusion compensation; and the edge computing platform is used for deploying a lightweight deep learning model to complete target recognition and trajectory prediction. According to the invention, through interdisciplinary collaboration of quantum dot materials, graph neural networks and physical equation constraints, the bottleneck of a single technology is broken through; and through closed-loop optimization of dynamic anti-interference and edge intelligence, full-link enhancement of'perception-decision-execution 'is realized.

A control method and system for precise landing of an unmanned aerial vehicle. The method comprises the following steps: on the basis of a multi-sensor fusion technique, using a lidar and a stereo vision algorithm to collect environmental data and perform primary processing, and generating comprehensive environmental perception data (step S1); on the basis of the comprehensive environmental perception data, using a digital elevation model algorithm to re-construct a three-dimensional terrain of a landing area, and generating a three-dimensional terrain model (step S2); on the basis of the three-dimensional terrain model, using an A* search path-planning algorithm to perform risk assessment and plan a safe landing path, and generating an optimized landing path (step S3); on the basis of the optimized landing path, using an adaptive control algorithm to adjust a flight attitude in real time by means of a fuzzy logic controller, and generating flight parameter adjustment (step S4); on the basis of the flight parameter adjustment, using machine vision and a decision tree algorithm to execute autonomous obstacle avoidance and emergency response, and generating a safe landing execution scheme (step S5); and on the basis of the safe landing execution scheme, using an ultrasonic sensor and a ground feedback system to confirm and fine-adjust a landing point, and completing final landing confirmation (step S6).