7893 results about "Lidar" patented technology

The invention discloses a multi-modal visual fusion complex scene small target detection tracking method and system, and relates to the technical field of unmanned aerial vehicle target tracking, and the method comprises the steps: employing a visible light camera, an infrared thermal imager and a laser radar sensor which are carried on an unmanned aerial vehicle platform, and synchronously collecting RGB images, thermal infrared images and point cloud data; the consistency of the multi-modal data is ensured through data preprocessing and space-time alignment; constructing a lightweight double-branch network to extract multi-scale features, generating a fusion feature map by adopting adaptive weighted fusion, and generating depth information by utilizing point cloud to assist in scale estimation; a small target detection head is designed based on the fusion feature map, and precise detection is realized in combination with a feature pyramid network, adaptive scale prediction and a context awareness suppression mechanism; furthermore, through multi-mode cooperative tracking, including target association, spatio-temporal context modeling, trajectory prediction and a re-detection mechanism, tracking continuity is ensured.

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

The invention discloses an unmanned aerial vehicle laser and vision fusion inspection method and system for bridge bottom disease detection, and the method comprises the steps: carrying out the synchronous data collection through employing a calibrated laser radar, a camera and an IMU, and obtaining a three-dimensional laser point cloud and a two-dimensional visual image of the appearance of a bridge; sharpening the image containing the motion blur and completing brightness self-adaption of the image; stable feature points are extracted, multi-frame matching is carried out, the corresponding poses of the images are estimated, and bridge dense point cloud reconstruction is completed; performing geometric component segmentation on the point cloud to generate a geometric prior region; component segmentation is carried out on a support area in the image, and a continuous and accurate component segmentation result is obtained in combination with a geometric prior area; screening the image, calling a targeted disease detection model in a corresponding component area, and generating a segmentation mask for the disease; obtaining a real disease three-dimensional point cloud, and carrying out quantitative calculation on the physical size of the disease; and displaying the real disease three-dimensional point cloud data and the physical size of the disease. The method is high in efficiency and precision.

The invention relates to the technical field of robot decision making, and provides a robot multi-modal fusion autonomous decision making method and system based on a large language model.The method comprises the steps that a robot obtains multi-modal environment information through a visual sensor, a touch sensor, an auditory sensor and a laser radar which are carried by the robot; performing preliminary filtering and noise reduction processing on the original sensor data, and synchronously recording all the sensor data by timestamps; performing space-time semantic alignment on the preprocessed multi-modal data, mapping pixel coordinates of a target in a visual target coordinate quantization original image to a robot coordinate system, performing uncertainty evaluation on a multi-modal signal through a dynamic Bayesian network, and taking entropy or variance as an uncertainty quantitative evaluation index. According to the method, the information quality is improved from a data fusion source, accurate and reliable basic support is provided for subsequent decision making, and decision making errors caused by data deviation are greatly reduced.

The invention discloses a visual language navigation method for cross-modal alignment in a dynamic shielding environment, and the method comprises the steps: collecting multi-modal data through a visual sensor, an inertial measurement unit, a laser radar and the like, and carrying out the preprocessing and time synchronization; sensing the dynamic shielding object through a model composed of a convolutional neural network and a long-short-term memory network, and estimating the future change of the dynamic shielding object in combination with a space-time sequence prediction algorithm; a double-branch convolutional neural network and a Transform based on a dynamic attention mechanism are adopted to respectively extract visual and semantic features and fuse the visual and semantic features; on the basis of occlusion prediction, potential occlusion region features are extracted in advance from a time dimension, an occluded image is repaired by using a generative adversarial network and geometric constraints in a space dimension, and cross-modal feature alignment is optimized through an attention mechanism; planning a path by using a hybrid reinforcement learning algorithm based on a deep Q network-space and a fast exploration random tree, and dynamically adjusting according to real-time shielding; according to the method, the accuracy, adaptability and reliability of visual language navigation in a dynamic shielding environment are improved.

The invention discloses an end-to-end automatic driving method based on dynamic multi-modal fusion in a complex scene, and belongs to the technical field of automatic driving. In order to solve the problems of sensor perception deficiency, cross-modal feature mismatching, unstable trajectory planning and the like easily occurring in night, low-illumination and complex dynamic environments in the existing end-to-end automatic driving method, texture details of a camera mode and geometric structure features of a laser radar mode are respectively enhanced through a double-flow feature refining mechanism; the characteristic difference between different modes is relieved; an information-driven dynamic fusion strategy is designed, the fusion weight is adaptively adjusted according to scene factors such as environment illumination and obstacle density, and the scene sensitivity and discrimination ability of the model are improved; asymmetric convolution and a low-rank-sparse decoupling technology are introduced, multi-order reconstruction of key channels is carried out on the multi-modal features, and the path change modeling capability is enhanced; and in combination with time sequence dependence of waypoints, outputting a future trajectory through an autoregression decoder to realize high-precision trajectory prediction and stable decision control.

The invention relates to the technical field of ground mobile unmanned equipment control, and discloses a ground mobile unmanned equipment autonomous obstacle avoidance control system optimized by artificial intelligence. The system comprises an environment perception layer, a bimodal risk assessment layer, a dynamic decision-making layer, a trajectory optimization layer and a feedback optimization layer. The environment sensing layer adopts a retina fovea centralis imitating mechanism to perform non-uniform sampling on laser radar point cloud data to generate dynamic point cloud partitions; the bimodal risk assessment layer fuses two types of radar data to generate static and dynamic obstacle risk assessment diagrams; the dynamic decision-making layer establishes space-time mapping and generates an obstacle confidence coefficient matrix through a graph neural network; the trajectory optimization layer converts the matrix into a control parameter based on a multi-objective evolutionary algorithm, and issues the control parameter through a time-sensitive network protocol; and the feedback optimization layer monitors environment change, calculates deviation, generates an effectiveness index, and dynamically adjusts a point cloud acquisition strategy until the index is optimal. According to the system, the autonomous obstacle avoidance capability and adaptability of the ground mobile unmanned equipment in a complex environment are enhanced.

The invention relates to the technical field of risk prediction, in particular to a collision risk prediction method of a low-altitude aircraft in high-density flight in a complex environment, which comprises the following steps: acquiring a point cloud through a three-dimensional laser radar, clustering to generate an obstacle trajectory, matching the trajectory to identify a disturbance characteristic segment, calculating a deviation angle by combining a path vector to generate a disturbance frequency graph, and calculating the collision risk of the low-altitude aircraft. And extracting a parameter modeling dynamic safety interval, and fusing multiple factors to evaluate a collision risk level. According to the method, obstacle trajectory topology is constructed through combination of three-dimensional laser radar point cloud time window division and density clustering, sudden change features are identified through trajectory similarity matching, a Gaussian kernel dynamic safety envelope is generated through combination of course offset statistics and included angle operation, and a self-matching threshold value is established through normalization parameters and radial basis weighting. The method improves the high-density flight collision prediction precision, enhances the weather and obstacle heterogeneity matching capability, reduces the misjudgment early warning delay, and achieves the multi-variable flight situation collaborative analysis.

The invention relates to the technical field of machine vision and perception, in particular to a ton bag lifting unmanned control system based on a binocular vision camera and a laser radar, which comprises an intelligent control unit, a lifting appliance executing mechanism, a sensing unit and a special ton bag, the sensing unit comprises a binocular vision camera and a laser radar and is used for collecting depth vision and three-dimensional point cloud information of an operation area; the intelligent control unit fuses multi-source data, locates a lifting lug by improving a weighted multi-feature fusion algorithm, plans a safety path and generates a staged instruction; the lifting appliance executing mechanism lifts and pulls a collapsed lifting lug through an electromagnetic adsorption module, a mechanical gripper module clamps the lifting lug, and reliable operation is achieved in cooperation with a verification mechanism; the special ton bag is matched with a sensing and executing module through a high-contrast color and a pre-embedded metal piece. The full-process unmanned operation is achieved, the robustness and operation safety of the complex environment are improved, and the ton bag hoisting requirements of multiple industries are met.

The invention relates to the technical field of land resource monitoring, in particular to a land resource dynamic monitoring and early warning method and system based on multi-source remote sensing data fusion, and the method comprises the steps: employing an unmanned plane to periodically collect optical images, SAR echoes and LiDAR point clouds, constructing a ground three-dimensional digital model, and carrying out the land parcel division; performing fusion to form a multi-dimensional feature vector, establishing an LSTM land parcel feature evolution model, and predicting a change rate interval of each feature in a current period based on a historical sequence; constructing a time sequence difference change detection algorithm, calculating a land parcel change rate, and screening potential abnormal land parcels by taking a prediction interval as an anomaly judgment threshold value; a double-branch convolutional neural network is adopted to identify crop states, growth stages and construction violation behaviors, abnormity is judged and determined, and confidence is given; spatial clustering is carried out on determined abnormal land parcels, accurate boundaries are obtained in combination with a three-dimensional model, multi-level early warning information is generated, and the decision-making efficiency and response speed of land resource monitoring are improved.

The invention discloses a multi-modal three-dimensional target detection method and device based on image-guided depth completion and multi-stage iteration fusion, and the method comprises the steps: firstly, predicting a dense depth map through an image-guided depth completion module by using the context information of an image, and carrying out the image-guided depth completion; the depth map is fused with a sparse depth map generated by the laser radar in a mask guiding manner, so that a high-quality complemented depth map is generated, and the accuracy of subsequent view angle conversion is improved; and then, through a multi-stage iterative fusion module, iterative fine-grained fusion is carried out on the converted image aerial view features and point cloud aerial view features, so that modal conflicts are effectively relieved, and the expression ability of fusion features is enhanced. According to the invention, through accurate depth information completion and efficient multi-modal feature fusion, the precision and robustness of three-dimensional target detection can be significantly improved, and especially the effect is more obvious when a long-distance target or a blocked target and other difficult targets are processed.

The invention discloses a landslide mass monitoring method based on an unmanned aerial vehicle laser radar. The landslide mass monitoring method comprises the following steps: S1, constructing an exposed earth surface point cloud model of a first time phase; s2, constructing an exposed earth surface point cloud model of a second time phase; s3, establishing a standard point cloud data set; s4, constructing a multi-dimensional time sequence feature vector sequence; s5, introducing a fusion prediction model of the ARIMA and the neural network, and outputting a final prediction sequence of the deformation trend of the landslide mass; and S6, constructing a disaster chain propagation model, outputting a landslide early warning grade according to a preset grading threshold value, and performing real-time early warning information release and response linkage. According to the invention, through fusion of laser radar point cloud registration analysis and ARIMA-neural network prediction modeling, high-precision dynamic monitoring and early warning of tiny deformation of the landslide mass are realized, and the method is suitable for landslide early identification and disaster emergency response scenes in a complex terrain environment.

The invention discloses an offshore platform anti-collision device and method based on multi-mode BEV perception, and the device comprises a three-dimensional fusion distance measurement module, a UWB distance measurement module, a ship navigation state monitoring module, a platform marine environment monitoring module, and a central processing unit. The three-dimensional fusion ranging module comprises a holder, a laser radar and a camera, and is used for performing 360-degree three-dimensional scanning on the periphery of the offshore platform; the UWB distance measuring modules are installed on the periphery of a ship and pile legs of an offshore platform. The ship navigation state monitoring module is used for acquiring ship position, navigational speed and course information in real time; the platform marine environment monitoring module collects wind wave data and weather data near the offshore platform and route information of passing ships through network communication. The invention discloses an offshore platform anti-collision device with real-time monitoring and collision risk assessment functions, the anti-collision safety of an offshore platform is improved, and the occurrence rate of collision accidents is reduced.

The invention relates to the technical field of slope settlement monitoring and early warning, in particular to a visual slope settlement monitoring and early warning method and platform. The method comprises the following steps: acquiring slope settlement monitoring data; preprocessing the acquired slope settlement monitoring data; respectively constructing a multi-scale slope digital twinborn model and a hybrid intelligent prediction model; constructing a multi-level early warning index system; constructing a fuzzy neural network early warning model based on a multi-stage early warning index system through a multi-scale slope digital twinborn model and a hybrid intelligent prediction model; and performing visual slope settlement early warning by using the fuzzy neural network early warning model. According to the space-air-ground integrated monitoring network constructed by the invention, a satellite InSAR, an unmanned aerial vehicle LiDAR and distributed optical fiber sensing are fused, and full-scale monitoring from regional macroscopic deformation to slope surface microcracks and deep soil displacement is realized.

The invention discloses a safety monitoring system of a high transverse support system for cable-stayed bridge man-shaped tower column construction, and relates to the technical field of bridge construction monitoring, the system comprises a creeping formwork integrated sensing module used for generating a point cloud model by using a mechanical arm integrated on a hydraulic creeping formwork platform and a laser radar scanning tower column curved surface, combining with a preset building information model coordinate, adaptively adjusting the mounting posture of the sensor, and outputting the mounting coordinate position of the sensor; the optical fiber sensing network module is used for collecting original strain and temperature data by using a distributed optical fiber sensor deployed along a main stress path of the support truss; an inertial navigation fusion positioning module; a digital twinning early warning module; and an edge calculation relay module. According to the invention, a full-process monitoring chain from data acquisition to risk early warning is constructed through cooperation of multiple modules, full-period and multi-dimensional dynamic control of construction of the human-shaped tower column high transverse support system is realized, and timely perception and overall control of potential risks are ensured.

The invention relates to the crossing field of mechanical engineering and automatic control, in particular to a global path planning and anti-swing control method for a ship unloader, and aims to solve the problems of rigid path planning and poor synergism of out-of-control swing of a lifting appliance in traditional ship unloading operation. The method comprises the following steps: constructing a dynamic three-dimensional operation space model with fusion of a laser radar and binocular vision, and updating obstacle information in real time; an improved fast extended random tree algorithm is adopted to generate a high-smoothness initial path; establishing a six-degree-of-freedom lifting appliance swinging dynamic model and identifying parameters on line; path tracking and anti-swing torque are synchronously optimized through a model prediction controller, and the control period is smaller than or equal to 20 milliseconds; and closed-loop feedback correction is implemented by combining an encoder and an inertial measurement unit. According to the scheme, environment dynamic sensing, path-anti-swing depth cooperation and multi-disturbance self-adaptive compensation are achieved, the operation rhythm is improved by 30% or above, the system still operates stably under the 8-level wind condition, and high precision, high robustness and engineering implementability are achieved.

A robot in accordance with at least some embodiments of the present technology includes a neck region through which the robot is configured to transmit and receive light. The robot defines a robot height and includes a body, a head carried by the body, and a neck extending between the head and the body along the robot height. The robot further includes a structural neck frame at a periphery of the neck in a plane perpendicular to the robot height. The structural neck frame defines windows distributed around the periphery of the neck. The robot further includes a sensor disposed at least partially within the structural neck frame. The sensor includes a laser configured to transmit light via the windows and a detector configured to receive light via the windows. A field of view of the sensor extends at least 320 degrees around the neck.

The invention relates to the technical field of surveying and mapping, and discloses a laser radar point cloud data processing surveying and mapping method and system based on deep learning, and the method comprises the steps: obtaining original point cloud data collected by a laser radar, carrying out the preprocessing of the original point cloud data, and obtaining the preprocessed point cloud data; taking a U-Net network as a basic framework, and introducing residual connection and a multi-scale feature fusion mechanism to carry out de-noising processing on the preprocessed point cloud data to obtain de-noised point cloud data; inputting the denoised point cloud data into a Point CNN-GAT model to extract local geometric features and a global topological relation, introducing a dynamic cavity convolution module, and adaptively adjusting the sampling range of a convolution kernel; registering the comprehensive feature vector output by the Point CNN-GAT model, and generating a corresponding surveying and mapping result according to the registered point cloud data; according to the invention, the time cost of data circulation and manual intervention is reduced, and the overall processing efficiency is improved.

The invention relates to the technical field of mining, and discloses a method for determining pre-splitting blasting parameters for mining blasting, which comprises the following steps of: 1, acquiring rock mass structure information of a blasting area through three-dimensional geological radar, laser radar and shallow seismic reflection data, and constructing a structural plane distribution map containing a soft and hard interbed and joint combination mode; 2, initial presplitting blasting parameters are generated based on the structural plane distribution map, wherein the initial presplitting blasting parameters comprise the hole distance, the linear charge density and the charge structure; according to the method, the technical scheme that the structural plane distribution map is constructed through multi-source geological data fusion and digital twinborn simulation dynamic correction is adopted, so that the technical effects that the influence of a complex structure combination on blasting energy transfer is accurately quantified, and the presplitting path control precision is remarkably improved are achieved; compared with the technical scheme depending on a static rock mass model or an empirical formula in the prior art, the defects of presplitting path offset and serious parameter mismatch caused by the fact that modeling is not carried out due to the dynamic effect of the structural plane are overcome.

The invention discloses a building facility detection data intelligent analysis and report system and method, and relates to the technical field of building structure health monitoring, and the method comprises the steps: achieving the high-precision data capture in a strong light dust raising environment through a laser radar and a dual-spectrum camera of a multi-mode anti-interference collection module; a millimeter wave radar array and an IMU inertial unit of the high-risk area strengthening module penetrate through severe working conditions to monitor key structure displacement; the credibility management engine is provided with an NFC timestamp chip and a double-chain block chain, and data judicial-level credibility and operation traceability are ensured; through humidity response gel packaging and Peltier semiconductor refrigeration of the self-maintenance sensing network, node environment self-adaption and drift suppression are achieved; and the multi-stage analysis center fuses edge-cloud computing power based on federal learning, generates a dynamic risk map and automatically outputs a compliance report. Accurate and timely reference is provided for operation and maintenance decisions, and the safety management level of building facilities is effectively improved.

The invention provides a multi-modal fusion and semantic enhancement train positioning method and system, and belongs to the technical field of rail transit, and the method comprises the steps: carrying out the time-space alignment of data, obtaining a dense point cloud, constructing a dense semantic point cloud, and dynamically estimating the confidence coefficient weight of each type of sensors; a set residual error and a Manhattan structure constraint residual error of a plane are constructed, laser radar point cloud parameters are obtained, and visual projection constraints are constructed at the same time; constructing a comprehensive degradation scoring function to carry out degradation judgment on the current environment; when the degradation result is yes, introducing a structure and motion information independent of an external environment, maintaining trajectory estimation, and constructing a compensation constraint; introducing a prior semantic constraint and a large model semantic factor constraint; and constructing a global optimization objective function, dynamically adjusting the weight of each modal factor, obtaining an optimal estimation state, and outputting a high-precision train positioning result. According to the method, high-precision and robust track estimation in an extreme scene is realized, so that the continuity, safety and intelligence of train positioning are guaranteed.

The invention relates to a robot path planning method based on multi-sensor fusion and free space topology composition, and belongs to the technical field of robot autonomous navigation. The method comprises the following steps: firstly, fusing laser radar and millimeter wave radar data, constructing multi-modal point cloud data, extracting a three-dimensional obstacle boundary by combining depth and normal vector mutation features, and generating a three-dimensional obstacle map and a free region tree; according to the constructed space model, factors such as energy consumption, dynamic obstacles, path smoothness and the like are integrated, target selection weights are dynamically regulated and controlled, and self-adaptive screening of intermediate navigation targets is achieved. And based on the intermediate navigation target, generating a safe trajectory satisfying dynamic constraints by adopting geometric-dynamic dual-mode fusion modeling, and enhancing the feasibility and robustness of the trajectory through trajectory envelope optimization and pre-execution fault-tolerant control. The method can be widely applied to autonomous navigation systems such as mobile robots and unmanned vehicles, and has good environment adaptability and path execution stability.

The embodiment of the invention discloses a multi-sensor fusion anti-degradation SLAM mapping method and system. The method can effectively solve the problem of pose drift of a robot in a mapping process in structure degradation environments such as an indoor long corridor, constructs a globally consistent three-dimensional point cloud map and a robot trajectory, and comprises the following steps: realizing depth coupling of an IMU and a wheel speedometer based on extended Kalman filtering, and generating high-frequency pose prediction; denoising, down-sampling and motion distortion correction are carried out on the 4D laser radar point cloud, and the normal vector and intensity characteristics of the point cloud are extracted; a normal vector and intensity feature enhanced scanning matching algorithm is adopted, and a target function is optimized through a multi-feature weight, so that the matching precision in a degradation scene is improved; loopback detection is realized through candidate key frame screening and geometric registration verification, and a closed-loop constraint is incorporated into a factor graph for global correction; and finally, incrementally updating the global point cloud map and carrying out consistency optimization, and outputting a robust three-dimensional point cloud map and a high-precision robot track.

The invention discloses a wharf shoreline terrain reconstruction method based on remote sensing data, and belongs to the technical field of three-dimensional terrain reconstruction. The method comprises the following steps: acquiring a multi-temporal multispectral remote sensing image, a synthetic aperture radar image, a laser radar point cloud and synchronous tidal water level data of a target wharf area; processing the multispectral image to generate an initial land and water segmentation map; carrying out tide correction by utilizing the tide water level data, fusing the multi-polarization characteristics of the synthetic aperture radar image, and correcting the ground feature type to obtain an accurate land and water boundary diagram; processing the laser radar point cloud to generate a digital surface model, extracting a shoreline contour with sub-pixel-level precision from the digital surface model, and endowing the shoreline contour with an elevation value; and superposing the shoreline contour with the elevation with the multispectral image to generate a three-dimensional terrain model. According to the method, the problems of low terrain reconstruction precision, large tide influence and insufficient automation degree of a traditional method in a complex wharf environment are solved, and high-precision and high-efficiency wharf shoreline three-dimensional automatic reconstruction is realized.

The invention relates to the technical field of road engineering safety monitoring, in particular to an expressway slope crack identification method based on unmanned aerial vehicle laser point cloud visualization, which comprises the following steps: multi-source data collaborative acquisition: an unmanned aerial vehicle carrying a laser radar scanner and a high-resolution optical camera flies along a multi-angle combined route, and the unmanned aerial vehicle carries a laser radar scanner and a high-resolution optical camera; synchronously acquiring side slope three-dimensional laser point cloud data and an orthoimage sequence; data fusion preprocessing: denoising and filtering the data to generate an exposed slope triangular mesh curved surface model, and mapping textures to generate a high-precision live-action three-dimensional model; performing multi-dimensional feature fusion recognition, extracting curvature and texture features, inputting the curvature and texture features into a pre-training classification model, judging and outputting a crack pixel-level position; and carrying out visual output, superposing crack information rendering and generating a quantitative report containing the length and width of the spatial position. The method is high in coverage precision and identification accuracy, and provides a reliable basis for slope safety assessment.

The invention provides a slope displacement monitoring data processing system based on an unmanned aerial vehicle laser radar, and relates to the technical field of data processing, and the system comprises the steps: carrying out the spatial interpolation processing of a topographic feature data set, and constructing a digital topographic surface model; the displacement field calculation module is used for performing iterative optimization based on a digital terrain surface model through spatial similarity analysis and fusion with a gradient descent algorithm, calculating a slope surface displacement vector field, identifying a potential sliding surface and a deformation abnormal region, and generating a displacement field calculation result; and the evaluation module is used for inputting a displacement field calculation result into a risk evaluation model and carrying out slope stability quantitative evaluation through a multi-source data fusion analysis platform. According to the invention, the practicability and operability of the monitoring result are improved.

The invention relates to the technical field of automobile brake control, in particular to a self-adaptive brake kinetic energy recovery control method and system based on multi-sensor fusion. Tire wear and road surface feature data are collected through a multi-source sensing fusion unit, and a tire wear coefficient and a road surface friction coefficient are generated through an intelligent decision calculation unit; the method comprises the following steps of: integrating the nonlinear correlation of the multi-source sensing fusion unit and the self-adaptive control execution unit through a rule and data fusion algorithm, outputting a comprehensive friction coefficient, and dynamically adjusting the braking kinetic energy recovery force and response time by the self-adaptive control execution unit according to the comprehensive friction coefficient. The system comprises a multi-source sensing fusion unit, an intelligent decision calculation unit, the self-adaptive control execution unit and a closed-loop feedback calibration unit. The closed-loop unit corrects data deviation through cross validation of the laser radar and the motor torque inversion model, the problems of poor working condition adaptation and unreliable data are solved, and the kinetic energy recovery efficiency, the braking safety and the driving smoothness are improved.

The invention discloses a roadside guardrail deformation detection method based on image and point cloud fusion and related equipment, and the method comprises the steps: collecting a guardrail region image through a high-definition camera carried by an unmanned plane, and synchronously collecting the point cloud data of a guardrail region through a laser radar; then, a joint calibration and iterative nearest algorithm is used for carrying out space-time registration on the guardrail area image and the point cloud data which are synchronously collected, and a multi-modal data set is generated, so that the problem of space-time dislocation caused by sensor movement is solved; an improved double-branch deep learning model is adopted to respectively extract image texture features and point cloud geometric features from a multi-modal data set, and the two features are fused to overcome a single-modal defect; and extracting the contour of the guardrail beam plate from the fusion feature map, aligning the contour of the guardrail beam plate with the reference model, and carrying out quantitative calculation to quantify the deformation degree of the guardrail and realize quantitative detection of the deformation of the guardrail.

The invention discloses a laser radar and multispectral polarization imaging fused target identification system and method. The system comprises a sensor configuration and data preprocessing module, a cross-modal feature extraction and fusion module and a multi-task output and optimization module. The sensor configuration and data preprocessing module performs time-space synchronization processing on the collected original optical signals and laser signals in the environment to obtain multispectral image data and laser radar point cloud data; the cross-modal feature extraction and fusion module performs feature extraction and fusion enhancement processing on the multispectral image data and the laser radar point cloud data, and outputs high-dimensional semantic enhancement point cloud representation containing image semantics and point cloud geometry; and the multi-task output and optimization module processes the high-dimensional semantic enhanced point cloud representation and outputs a three-dimensional target recognition result, target speed information and a pixel-level depth map. Through module design and data processing, the defects in the prior art are overcome, and the accuracy of target recognition is improved.

A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and / or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and / or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and / or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.