238 results about "Terrain mapping" patented technology

Locator systems are disclosed for locating buried utilities such as conduits, cables, pipes. or wires. The locator system may include image-capturing and position and orientation measuring devices such as magnetic compass, accelerometers, gyros, GPS, and DGPS. The system may associate images captured during the locate process with buried utility position data and the other sensor data. These data and images may be further associated with terrain images from satellites or aerial photography to provide a highly precision map and database of buried utility locations and visual images of the burial site. Information so obtained may be transferred to a hand-held personal communication device, such as a smart phone to show the location of buried utilities in combination with photo-images and / or terrain maps.

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

The invention provides a high-precision topographic mapping and three-dimensional modeling system based on an unmanned aerial vehicle, and the system comprises an unmanned aerial vehicle cooperation module which is used for deploying a multi-source sensor and a sensor electronic interface on the unmanned aerial vehicle, and building a distributed network of the unmanned aerial vehicle; the real-time dynamic positioning module is used for acquiring unmanned aerial vehicle positioning data and an initial target terrain map; the autonomous control module is used for generating a flight path, performing dynamic adjustment, constructing an autonomous flight control model and outputting a flight control instruction; the data quality enhancement module is used for performing parameter adjustment on the real-time data and performing space and signal level combined processing on the real-time data so as to enhance the data quality; and the terrain model optimization module is used for processing the laser radar point cloud data so as to construct a three-dimensional terrain model post-processing module, and carrying out data visual display and flight suggestion generation. According to the method, the positioning and modeling precision is greatly improved, efficient surveying and mapping are achieved, the single-point fault risk is reduced, and it is guaranteed that tasks can be completed in a high-safety, high-precision and intelligent mode.

The invention discloses a robot gait self-adaptive adjustment method and system combined with topographic feature recognition, and relates to the technical field of robot gaits, and the method comprises the steps: collecting a topographic image in front of a robot advancing path, carrying out the deblurring processing, and extracting ground feature data through a lightweight neural network; matching the ground characteristic data with a preset gait control parameter database to obtain a gait control parameter set, and generating a control signal through a proportional-differential control method according to the gait control parameter set; correcting the control signal based on a reinforcement learning algorithm, and generating a target control instruction for driving a joint actuator; in the process of executing the target control instruction, collecting state feedback data in real time, and comparing and optimizing the feedback data with the currently executed target control instruction to realize feedback adjustment; the problems of insufficient terrain recognition precision caused by image blurring, response lag caused by sensing and control decoupling and lack of adaptive ability are effectively solved.

The invention discloses a multi-modal space-time fusion and distributed cluster fire extinguishing robot cooperative control system, aims to solve the problems of low efficiency and poor precision of multi-machine cooperative fire extinguishing in wide-area fire investigation and flammable and explosive scenes, and provides a whole set of unmanned fire extinguishing technical solution for the multi-mode space-time fusion and distributed cluster fire extinguishing robot cooperative control system. The system comprises four core hardware architectures, namely a cloud dispatching center (A), an unmanned aerial vehicle cluster (B), an unmanned resource guarantee vehicle (C) and a crawler-type fire extinguishing robot cluster (D), and has the following core functions: 1, the cloud dispatching center (A) updates a fire scene model through distributed dispatching by fusing multi-modal sensor data, dynamically allocates tasks and plans a safe approach path; 2, the unmanned aerial vehicle cluster (B) executes the instruction, completes fire global positioning and cluster cooperative sensing, generates a high-precision fire topographic map and feeds back a signal; thirdly, the unmanned resource support vehicle (C) autonomously says for help, and has the functions of automatic mechanical arm water feeding and power supply, environment detection, on-site cleaning after fire extinguishing and communication relay; and fourthly, the crawler-type fire extinguishing robot cluster (D) autonomously navigates and positions the root of a fire source, detects and predicts the drop point of a water column, carries out fire extinguishing feedback evaluation, and interacts with the dispatching center cooperatively to dynamically adjust the strategy, so that closed-loop control is formed, and the fire extinguishing efficiency and accuracy are remarkably improved.

The invention, which relates to the technical field of the unmanned aerial vehicle technology, discloses an unmanned aerial vehicle take-off and landing comprehensive electro-hydraulic drive control system comprising the following components: a data acquisition module, a deep learning model module, a drive control strategy generation module, and an electro-hydraulic drive execution module. Comprehensive environment information and historical take-off and landing data of the unmanned aerial vehicle are collected through the data acquisition module, a deep learning model is used for accurate prediction, an optimal driving control strategy is generated, in the take-off process, the system can accurately calculate the needed initial driving pressure and speed, dynamic adjustment is carried out according to different environment factors, and the driving control precision of the unmanned aerial vehicle is improved. According to the comprehensive electro-hydraulic driving and controlling system, the unmanned aerial vehicle can stably take off, when the unmanned aerial vehicle lands, a terrain perception and obstacle avoidance mechanism is introduced into the system, a three-dimensional topographic map is constructed in real time, obstacles are detected, a safe landing path and a driving and controlling strategy are generated, the landing accuracy and safety are improved, and the comprehensive electro-hydraulic driving and controlling system remarkably improves the taking-off and landing precision and stability of the unmanned aerial vehicle.

The invention discloses a topographic surveying and mapping method and system based on unmanned aerial vehicle cluster collaboration, and belongs to the technical field of unmanned aerial vehicle cluster collaboration topographic surveying and mapping, and the method comprises the following steps: a ground control center plans a flight route for each unmanned aerial vehicle in a cluster according to the topography and precision requirements of a surveying and mapping area, a theoretical processing sequence of data of each unmanned aerial vehicle is calculated in advance; and the unmanned aerial vehicle cluster autonomously takes off to a predetermined airspace according to the instruction, and dynamically establishes a multi-hop relay communication network based on a mobile ad hoc network technology in a flight surveying and mapping process so as to evaluate and use the fastest signal transmission path. Through the combination of the steps, the data processing and transmission efficiency is improved, reliable communication and collision prevention are guaranteed, weather interference can be dealt with, and efficient and accurate topographic surveying and mapping are achieved.

The invention relates to the technical field of hydrological data measurement, in particular to a sky-ground-water integrated hydrological monitoring system. The system comprises the following modules: a space-based monitoring module, an air-based monitoring module, a foundation monitoring module, a water-based monitoring module and a hydrological early warning module. Specifically, a hydrological monitoring area is patrolled through a satellite platform, and terrain height data is monitored in a dot-matrix mode; and scanning the hydrological monitoring area along the sine wave path through the unmanned aerial vehicle, and generating a three-dimensional topographic image in combination with the topographic height data. According to the invention, through a data processing technology, a real-time monitoring technology and a deep learning technology, three-dimensional, real-time and accurate monitoring of water level, flow, terrain and sediment hydrological elements is realized; scientific, reliable and comprehensive data support is provided for the fields of water resource management, flood control and disaster reduction and ecological environment assessment; meanwhile, a hydrological early warning model is constructed, and hydrological abnormal conditions are captured in time.

The invention discloses a simulation method for urban flood emergency rescue path planning, and the method comprises the steps: carrying out the modeling of an urban road network based on an urban topographic map, rainfall data, existing water distribution and key position information; mIKE simulation software is used for establishing a hydrodynamic model to simulate the rainfall and flood spreading process of the city, and flood dynamic information, including the depth and flow velocity of the flood, at each position point in the flood disaster scene model is obtained; respectively constructing objective functions according to the shortest maximum time of arrival of the disaster relief point, the lowest driving risk of the rescue vehicle and the shortest total weighted rescue time; setting constraint conditions to ensure that all disaster relief points are covered, and preventing vehicles from being excessively distributed; based on the flood dynamic information, the objective function and the constraint condition, adopting an NSGA-II non-dominated sorting genetic algorithm to solve a Pareto solution set; and performing weighted analysis on a result in the Pareto solution set to obtain an optimal solution, and outputting an urban flood emergency rescue path plan.

The invention provides a quadruped robot path optimization method and system, and the method comprises the steps: building an overlapping operation window recognition mechanism through obtaining the state information and topographic data of a robot, generating a risk map based on a conflict probability, and constructing a hierarchical task chain; establishing path-terrain mapping, identifying a vortex region through an energy flow channel and flow field rotation characteristics, and forming a path dominant region; performing task density analysis on the dominant region, and forming a complementary rhythm through peak-valley separation and frequency analysis to generate an equilibrium flow; main and auxiliary path assignment is carried out on the balanced flow, and high-density path reserve is formed through load analysis and compression processing; performing risk detection on the main path to obtain an early warning, triggering reserve release according to the early warning, and performing cross weaving with the main path to form a reinforcing network; a cooperative propagation matrix is constructed based on a reinforcement network, flow field diffusion analysis is carried out to generate discrete path configuration, a convergence center is established, a unified control command is output, and the cooperative path optimization problem under the complex terrain is effectively solved.

The invention discloses a time sequence prediction and capacity bottleneck early warning method based on deep learning, and relates to a cloud system and a data processing technology, and the method comprises the steps: obtaining static equipment attribute data and a dynamic index of the cloud system; constructing a resource topological map, and establishing a terrain-like map covering each cloud system; dividing a normal equipment group and a capacity bottleneck associated equipment group based on the terrain-like elevation in the terrain-like map; respectively extracting topographic index features of the associated equipment group and the normal equipment group; utilizing parallel LSTM prediction channels to respectively predict the extracted topographic index features, and dynamically fusing the output of the dual prediction channels; and comparing a fusion output result with a preset capacity threshold value so as to execute dynamic capacity bottleneck early warning in the terrain-like map. According to the method, the service calling chain, the data flow direction path and the equipment association state are displayed in a three-dimensional mode in combination with the visualization technology, and multi-node capacity bottleneck early warning is achieved.

The invention discloses an unstructured environment motion planning method, device and equipment, and relates to the field of automation technology and vehicle engineering, and the method comprises the steps: inputting a to-be-tested unstructured environment pavement topographic image into a neural network model, determining the probability of any terrain type to which each pixel in the to-be-measured unstructured environment pavement topographic image belongs; adopting a Bayesian regression model to determine comprehensive passable risks under different road surfaces according to the probability of any terrain type to which each pixel belongs; an entropy risk measurement method is adopted to convert the comprehensive passable risks under different road surfaces into quantitative risks, and a passable map is generated; according to the trafficability map, determining trafficability parameters of the motion planner based on the cross entropy; according to the method, a motion planner based on cross entropy is utilized to perform motion planning on a vehicle in a to-be-detected unstructured environment according to the trafficability parameters, so that comprehensive probabilistic evaluation on terrain risks is realized, and the defects of uncertainty of terrain perception and terrain adaptability are overcome.

The invention relates to the technical field of traffic control systems, in particular to a traffic energy efficiency control system integrating three-dimensional topographic surveying and mapping and vehicle-road cooperation, which comprises a three-dimensional road network topographic sensing module, a road side unit and a vehicle-mounted unit, a vehicle position and speed information and original point cloud data of a surrounding environment are received by the three-dimensional road network topographic sensing module; and giving gradient and curvature coordinates to each road section in the road network, and generating a vehicle terrain association topological graph. According to the method, quantitative assignment of road gradient and curvature coordinates is realized by fusing vehicle position, speed information and spatial geometric description established by point cloud, and a mapping relation is established between topographic elements and a vehicle running state, so that a vehicle path and topographic attributes are synchronously linked. In the emergency response, the slope angle and the driving direction angle of the affected vehicle are used for constructing an energy consumption priority sequence, and evacuation path selection under the energy efficiency dimension is completed from the motion relation between the vehicle and the road.

The embodiment of the invention provides a landslide susceptibility prediction method, system and device and a computer readable storage medium. The method comprises the steps of obtaining a to-be-divided topographic image; wherein the topographic image is a slope direction and mountain shadow map extracted based on the digital topographic image; performing region division on the topographic image through a multi-scale algorithm to obtain slope units; wherein the multi-scale algorithm is used for carrying out slope unit division on the terrain; based on the slope unit, environment factor extraction is carried out, and multiple evaluation factors are determined; performing landslide susceptibility prediction on the slope unit based on the plurality of evaluation factors through a landslide susceptibility prediction model to obtain a landslide susceptibility prediction result corresponding to the slope unit; wherein the landslide susceptibility prediction model comprises a self-organizing mapping neural network module, an information amount module and a support vector machine module; the landslide susceptibility prediction model is a model which is trained by adopting historical landslide data and is used for carrying out landslide prediction on the slope unit. According to the scheme, the accuracy of landslide susceptibility prediction can be improved.

The invention discloses an all-terrain maneuvering target tracking system and an all-terrain maneuvering target tracking method, belongs to the technical field of all-terrain moving target tracking, and aims to solve the problems of poor target tracking trajectory adaptability and large vibration interference in a complex terrain. During operation, the target orientation is positioned, the advancing direction is updated in real time, after the collection range is dynamically delimited, multiple frames of images are collected, and a topographic map is constructed through cutting, splicing and distortion correction; preprocessing the topographic map, extracting landform features, and dividing passable areas; generating a tracking trajectory in the passable area, and optimizing the trajectory through the position change of the target; and adaptively adjusting parameters of the balance component according to the track area, and fusing a vibration signal to judge whether to trigger track updating so as to ensure that the track dynamically adapts to terrain and target movement. According to the invention, through enhancing image processing and multi-source data fusion, the continuity, accuracy and environmental adaptability of target tracking in a complex terrain are significantly improved, and the reliability and efficiency of operation are effectively enhanced.

The invention relates to a terrain adaptive navigation method and system of a robot, and relates to the technical field of robot terrain navigation, and the method comprises the following steps: obtaining the moving speed of the robot in a past time window, collecting current moving road surface data, and identifying to obtain a road surface topographic map; randomly selecting a first moving position, obtaining a corresponding first moving speed and amplitude, and obtaining a first moving stability coefficient in combination with the first pavement topographic information; configuring a movement error range, dividing and obtaining a first movement position range, and calculating and obtaining a first fusion stability coefficient according to the first range pavement topographic information; first navigation fitness is obtained through calculation, and the optimal movement position is obtained through movement position optimization selection. The problems that in traditional terrain self-adaptive navigation, the safety and smoothness of the robot in the moving process are difficult to balance, the toppling risk is high or the advancing rhythm is frequently interrupted in the moving process, and the navigation effect is affected are solved.

A system and method for calibrating and setting up a machine guidance system for construction assets, such as loaders or excavators, employ point cloud data from onboard optical sensors to acquire spatial measurements. Point cloud data is transformed to an asset coordinate frame, candidate pitch and roll offset values are applied, grid representations are generated, and calibration values are selected with reduced error by comparing these grids to a reference surface. The system and method support autonomous operations including obstacle avoidance and grading by modifying the point cloud data according to the determined calibration values. In addition, dual-sensor calibration, terrain mapping, and filtering techniques are disclosed.

The invention relates to the technical field of data processing, in particular to a linear motor positioning precision dynamic test and analysis method, which comprises the following steps of: controlling a linear motor to perform full-stroke reciprocating constant-speed scanning, synchronous high-frequency acquisition of positions, current instructions and speed data; resampling according to a fixed position interval space to generate forward and reverse current and speed sequences; utilizing the characteristic that the friction force directions are opposite to counteract the friction component, calculating the resistance characteristic value of each position by combining the two-way speed stability weight, and constructing a resistance topographic map; the magnetic cogging effect and the mechanical anomaly are decoupled through the space gradient and the statistical deviation, and the mechanical anomaly saliency is determined; and a current feed-forward compensation table is generated and written into a driver, and a fault is marked for a saliency threshold exceeding position, so that dynamic testing and analysis of positioning precision are realized. According to the method, the resistance component is accurately separated by combining the current algebraic operation and the bidirectional speed weight, the topographic map is constructed to decouple the tooth socket and the installation abnormity, and the dynamic test precision and the diagnosis practicability are improved.

The invention relates to the technical field of power transmission line design, in particular to a temporary road design method and system suitable for mountainous area power transmission line construction. Comprising a data interaction module used for downloading topographic data, engineering basic data and a three-dimensional digital design result from a server; the terrain rendering module is used for performing rendering processing on the current engineering terrain and ranking data; the calculation rule module is used for adjusting boundary conditions and planning design conditions of road path planning; the path algorithm module is used for calculating a construction road path most suitable for the current tower position according to given input data and calculation rules; the path plotting module is used for automatically plotting in a topographic map according to the calculated construction road path; the road attribute module is used for carrying out road attribute setting and road segmentation setting on the current road; and the path adjustment module is used for modifying the road path design result. According to the invention, automatic construction road path planning can be realized, and the road engineering quantity is calculated.

The invention relates to the technical field of robot control, in particular to an electric drive column type wheel-foot robot self-adaptive obstacle crossing control method based on advancing path recognition, and the method comprises the following steps: S1, environment perception and terrain map generation: generating a terrain map; s2, mode switching and judgment: according to the topographic map, in combination with a preset wheel-foot mode switching threshold value, judging that the current obstacle type is a climbing platform, a slope ridge or a crossing ditch; s3, setting each mode parameter; s4, dynamic stability checking and correction preprocessing: according to the current gravity center position of the robot, the obstacle crossing stability is evaluated, and if the overturning risk exists, setting is automatically adjusted. According to the method, typical obstacle types such as platform climbing, ditch crossing and slope ridge can be accurately identified, and compared with a traditional method which only depends on a single sensor or manually presets a threshold value, the method has higher environmental adaptability and obstacle crossing scene judgment precision.

The invention relates to a dam burst scene unmanned aerial vehicle monitoring method and system based on artificial intelligence. The method comprises the steps that hydrological data, meteorological data, satellite image data and topographic map data are acquired and input into a monitoring area identification artificial intelligence model, and target monitoring area risk level information and target monitoring area detection range information are generated; distributing the number of unmanned aerial vehicles and a preset route of each unmanned aerial vehicle based on the target monitoring area risk level information and the target monitoring area detection range information; controlling each unmanned aerial vehicle to fly along a corresponding preset route, and acquiring target monitoring data of each unmanned aerial vehicle; and inputting the target monitoring data of each unmanned aerial vehicle into the dam bursting and breaking scene analysis artificial intelligence model to generate a dam bursting and breaking scene analysis result. By adopting the method, high efficiency, accuracy and real-time performance of dam burst monitoring can be realized through multi-source data driving, dynamic task allocation and artificial intelligence model optimization.

The invention discloses a general game development engine system based on OpenGL (Open Graphics Library). The general game development engine system comprises an application program control module and an application program module, the application program module comprises an input processing module, a terrain module, a game AI module, a GUI graphical interface module and a rendering output module; the control module is used for initializing an OpenGL environment and calling a terrain in the program module, and the GUI module is used for initializing game pictures; calling an input processing module to obtain input data of a user, and calling a game AI module to perform interaction between the user and a game picture; updating data of the terrain, the GUI, the sound effect and the game AI module; a rendering output module is called to form a specific game picture and render the game picture, a game cycle of initialization-data updating-picture rendering is constructed, and a complete game cycle is formed; the 3D / 2D game engine framework based on the OpenGL is created by integrally designing components and modules necessary for a general game engine, and a game project is quickly constructed.

The invention provides an unmanned aerial vehicle carrying system for complex terrain surveying and mapping, and relates to the technical field of unmanned aerial vehicle surveying and mapping. The unmanned aerial vehicle carrying system for complex terrain surveying and mapping comprises a sensor unit, the sensor unit comprises an integrated laser radar, a multispectral camera, a double-antenna RTK. PPK positioning module and an intelligent control unit, and the intelligent control unit comprises an environment sensing processor, a dynamic path planning module, a multi-source data fusion engine and a power system. The power system comprises an oil-electricity hybrid module, the oil-electricity hybrid module is composed of a lithium battery and a magnetic suction type fuel oil range extender, the magnetic suction type fuel oil range extender is mounted on the belly through a magnetic suction connector, and the connector comprises an axial positioning pin and a rare earth magnetic ring. Oil-electricity seamless switching is achieved through the magnetic attraction quick-release range extender, the endurance is greatly improved, the multi-degree-of-freedom posture adjusting platform is matched with the millimeter-level sensor for hard synchronization, and vibration distortion is improved.

A machine guidance system and method utilize optical sensors, position sensors, and movement sensors to generate point cloud data for a construction asset. Data encompassing terrain features, obstacles, and the real-world position and orientation of the asset and its attachment are calculated. The fused data is used to autonomously control the movement of the asset and the position of the attachment to maintain a cutting edge at a target grade. As a result, terrain mapping and obstacle detection are performed in real-time, enabling safe and efficient operation without predefined paths. In this manner, applications include automated earthmoving, grading, and material handling in dynamic construction environments.

The invention relates to the technical field of mechanical engineering and intelligent control, and particularly discloses a dynamic leveling method and system for a damping profiling icebreaking snow remover. The method comprises the steps that high-strength alloy impact toughness parameters and staggered rolling path parameters are collected, an icebreaking wheel set rigidity matching matrix is generated through optimization, and adjustable rigidity assignment is completed; acquiring road surface fluctuation data within a profiling distance based on a laser ranging device, and fusing multispectral and millimeter wave radar data to generate a three-dimensional dynamic terrain map; inputting the rigidity matching matrix and the dynamic terrain map into an LSTM mechanical simulation engine, calculating five-wheel-set dynamic pressure distribution and outputting real-time wheel set pressure parameters; the real-time pressure parameter is compared with the measured value of the sensor, and an oil cylinder stroke compensation instruction is generated to drive hydraulic leveling control; analyzing the three-dimensional position offset of the snow shovel and the ice breaking wheel, converting the three-dimensional position offset into a side row inclination angle parameter, and generating a multi-shaft torque cooperative control instruction; and generating a material wear characteristic spectrum through federal learning training, and dynamically correcting a path inclination angle and a rigidity compensation parameter. The ice breaking and snow removing device has the beneficial effects of improving ice breaking and snow removing efficiency, enhancing equipment stability and reducing material abrasion.

The invention relates to the technical field of unmanned ship surveying and mapping, in particular to a mud flat topography surveying and mapping method and system based on a water surveying and mapping unmanned ship, and the method comprises the following steps: S1, setting the area range of mud flat topography surveying and mapping; s2, collecting spectral information of different wavebands to generate water surface feature data of water surface brightness, texture and coverage states; s3, obtaining topographic data of the bottom of the mud flat, and carrying out real-time calibration on the sonar data; s4, performing manifold alignment on the water surface feature data and the sonar water bottom data; s5, constructing a three-dimensional terrain model of the mud flat area; and S6, comparing the generated three-dimensional terrain model with historical surveying and mapping data, and dynamically adjusting the terrain model through an adaptive calibration algorithm. According to the invention, through autonomous navigation, real-time data calibration and multi-dimensional data fusion technologies, high-precision, dynamic and visual surveying and mapping of the mud flat terrain are realized, and the data acquisition stability and the accuracy of the terrain model are improved.

Apparatus and methods for mitigating slip conditions and estimating ground friction for a robot having a plurality of feet are provided. In one aspect, a method includes estimating a coefficient of friction for a ground surface supporting the legged robot based on sensor data, odometry data, and a terrain map of an environment. The sensor data includes a set of joint angles and a set of joint torques for a set of joints of the legged robot, and the odometry data indicates a location of the legged robot in the environment. One of the plurality of feet of the robot applies a force on the ground surface based on the estimated coefficient of friction.

The invention provides a cooperative control method for a hexapod robot and a bionic mechanical arm, and the method comprises the steps: firstly transmitting a laser pulse through a laser radar at high frequency, and generating three-dimensional point cloud data based on a plurality of distance measurement points in a space; performing denoising, registration, filtering, simplification and data fusion on the data to construct a complete three-dimensional scene model; generating a digital topographic map containing terrain surface and obstacle information through feature extraction; the main control component controls the movement of the multi-joint legs based on a terrain model: in the walking stage, in combination with global path planning and a local obstacle avoidance algorithm, static obstacle avoidance and dynamic obstacle real-time avoidance are realized; and after the robot reaches the vicinity of the target, the main control component controls the mechanical arm of the robot to grab, and the angle of each joint of the mechanical arm is adjusted by utilizing path planning and an obstacle avoidance algorithm, so that the mechanical arm avoids the obstacle, and the mechanical arm can accurately grab the target object.

The invention relates to the technical field of three-dimensional modeling and geographic information processing, and discloses a three-dimensional city model generation optimization method and system, and the method comprises the steps: obtaining a central city area three-dimensional data set of a target city; generating a second fusion data set of unified three-dimensional point cloud representation; based on the key feature point set, a random consistency verification mechanism is adopted to execute preliminary registration operation; and generating a high-precision three-dimensional city model. In the prior art, in a multi-source three-dimensional data fusion process, coordinate system offset and scale difference exist among an oblique photography image, LiDAR point cloud, BIM data and a topographic map, and especially under a large-scale central urban area modeling condition, high-precision and automatic unified alignment is difficult to realize. And dislocation of building boundaries or fracture of terrain curved surfaces are caused. Due to the fact that depth feature extraction and cross-source feature alignment are combined with random consistency verification, the space consistency and modeling reliability of the three-dimensional city model are improved.

The invention provides a satellite-borne array laser radar three-dimensional topographic surveying and mapping data comprehensive analog simulation method and system. The method comprises the following steps: establishing a satellite-borne array laser radar three-dimensional topographic surveying and mapping simulation link; constructing a satellite-borne array laser radar comprehensive analog simulation model, and generating a satellite-borne array laser radar three-dimensional terrain simulation data set; and evaluating the quality of the satellite-borne array laser radar three-dimensional topographic surveying and mapping analog simulation data under the condition of configuring different parameters by adopting a plurality of evaluation indexes such as noise rate, average signal photon number, detection probability, signal-to-noise ratio and elevation precision. The invention designs a satellite-borne array laser photon cloud data analog simulation method for ground laser angle reflection (CCR). A set of satellite-borne array laser radar three-dimensional terrain simulation data set is generated by taking a certain type of domestic satellite parameters as an example; a plurality of evaluation indexes are adopted to evaluate the quality of satellite-borne array laser analog simulation data under the condition of configuring different parameters, and a solid foundation is provided for developing array laser three-dimensional topographic mapping satellites.