181 results about "Simultaneous localization and mapping" patented technology

The invention discloses a dynamic semantic SLAM (Simultaneous Localization and Mapping)-driven robot full life cycle navigation method, which comprises the following steps: synchronously acquiring multi-modal data through a multi-modal sensor array, and carrying out joint coding based on a cross-modal contrast learning framework to obtain embedded features. A dynamic three-dimensional Gaussian radiation field is constructed, a scene is represented as a parameterized Gaussian kernel set, and mixed Gaussian field representation is obtained through micro rendering and joint training optimization. A Gaussian mixture field is divided into a global static layer and a local dynamic layer, retention constraints are applied to the static layer, and an incremental online updating strategy is adopted for the dynamic layer. Based on the optimized Gaussian field parameters and the multi-modal data, a factor graph containing Gaussian rendering residual factors and inertia pre-integration factors is constructed and solved, the corrected robot pose is obtained, and the space occupancy probability is generated. Operation control parameters are adjusted on line according to the pose and the occupancy probability through a strategy network, and autonomous navigation is achieved in combination with path planning and trajectory tracking.

The invention relates to the technical field of computer vision, and particularly provides a tunnel video stream three-dimensional modeling system based on binocular stereo matching and SLAM (Simultaneous Localization and Mapping), which comprises a multi-modal image acquisition unit, a binocular infrared camera and an RGB (Red, Green and Blue) camera are configured to synchronously acquire an infrared image pair, an RGB image pair and inertial measurement unit data of a tunnel environment, the multi-source data is uploaded to the cloud processing platform in real time through the wireless transmission module; based on a dynamic switching mechanism of environmental perception, performing adaptive fusion pose estimation of a feature point method and a direct method on input multi-modal image data; the stereo matching and dense reconstruction unit is used for calculating a disparity map by adopting an improved self-adaptive window stereo matching algorithm, generating a three-dimensional point cloud in combination with the pose information output by the pose estimation unit, and constructing a global consistency point cloud model of the tunnel scene through a time sequence point cloud registration and splicing algorithm; and a multi-algorithm target detection and semantic fusion unit. The method can meet the requirements of precision and robustness of tunnel reconstruction.

A method for motion synchronization-based head pose estimation, by a head mounted display (HMD) device and an HMD device for performing the same are provided. The method includes receiving, by the HMD device, motion data from a plurality of motion sensors of the HMD device, receiving, by the HMD device, a plurality of image frames from at least one simultaneous localization and mapping SLAM camera of the HMD device, estimating, by the HMD device, a plurality of motion parameters of head movements of a user from the plurality of image frames received from memory, generating, by the HMD device, a filtered subset of the motion data received from the plurality of motion sensors based on the plurality of motion parameters of the head movements, synchronizing, by the HMD device, the plurality of image frames received from the memory and filtered subset of motion data, and estimating, by the HMD device, the head pose based on the synchronized plurality of image frames and motion data.

The invention discloses a narrow environment high-precision laser inertial navigation and SLAM (Simultaneous Localization and Mapping) method based on adaptive parameters. According to the method, firstly, radar and IMU data are collected and preprocessed to form a unified input data stream; obtaining a pose prediction result by using the data stream, constructing an observation model, and obtaining a laser observation residual error; calculating a characteristic value proportion in the matching process of the point cloud and the sub-map, and judging a geometric degradation scene; when degradation is detected, adaptively adjusting a voxel filtering radius and a loopback detection threshold value; fusing the pose prediction result and the laser observation residual error by adopting a Kalman filtering model to obtain an optimal pose estimation result, and carrying out loopback detection and back-end map optimization under a dynamic threshold value to correct accumulated drift and update a voxel map; and finally outputting a continuous pose track and a three-dimensional voxel map. According to the method, the robustness and the positioning precision of the laser inertial navigation fusion SLAM in narrow scenes such as pipe galleries, tunnels and chemical plants are effectively improved.

The invention provides an unmanned vehicle local high-precision positioning mapping system and method based on reinforcement learning polarization normal deambiguity and depth information fusion, and the method comprises the steps: synchronously obtaining a polarization image and a depth image of a target scene in the driving process of an unmanned vehicle in a low-texture and low-illumination scene; constructing the polarization normal candidate, the depth normal, the neighborhood surface curvature and the depth gradient information into a state vector, inputting the state vector into a reinforcement learning agent based on Dueling DQN, and obtaining the depth gradient according to a reward function fusing the included angle error of the polarization normal and the depth normal, the neighborhood normal smoothness loss and the consistency error of the polarization normal and the depth gradient direction. Outputting an optimal polarization normal selection action to obtain an unambiguous surface normal; polarization estimation depths are generated, and confidence coefficients are calculated respectively; according to the depth confidence coefficient and the polarization confidence coefficient, pixel-level weighted fusion is carried out, a fused depth map is obtained, synchronous positioning and mapping are carried out on the fused depth map and the synchronous RGB image, and a point cloud map of the target scene and the moving track of the unmanned vehicle are output.

The invention discloses an SLAM (Simultaneous Localization and Mapping) optimization mapping method and device combining geometric verification and constraint of a reflector, relates to the technical field of industrial-grade mobile robot navigation, and solves the problem that in the prior art, a continuous constraint mechanism cannot be established in a mapping process to correct accumulative errors in motion. The method comprises the following steps: acquiring an original laser radar point cloud containing a reflector, screening out reflection points with the distance smaller than a preset distance, and carrying out clustering and quintuple geometric verification on the screened points by adopting a density clustering algorithm, so as to obtain a multi-dimensional geometric verification mechanism based on PCA (Principal Component Analysis); local coordinates of the center of the reflector are calculated and converted into global coordinates, the reflector is used as a stable Landmark depth to be fused into an SLAM back-end graph optimization framework, and optimization nodes including a timestamp, a robot pose, a reflector ID and an observation pose are constructed based on a global coordinate system to participate in graph optimization; and the matching problem caused by insufficient natural characteristics or environment change in a long corridor scene can be solved in a targeted manner.

The invention discloses a map construction method of a three-dimensional Gaussian SLAM (Simultaneous Localization and Mapping) algorithm based on depth information fusion, which comprises the following steps of: firstly, adopting a dynamic point cloud downsampling method based on depth information, so that the defect that structural information is easy to lose due to the fact that point cloud spatial distribution and geometric characteristics caused by the depth information are not considered in traditional random downsampling can be overcome; the sampling rate is dynamically adjusted through depth information, a key geometric structure is reserved while data redundancy is reduced, good geometric priori is provided for generation of Gaussian point clouds, and the generated Gaussian point clouds are more fit with the surface of an object; moreover, according to the method, the optimization efficiency in the map reconstruction process is improved through the perspective principle of depth information adaptive point size and simulation of human eye observation, and the geometric error of luminosity rendering of the reconstructed map is smaller; in addition, the depth information is optimized by utilizing multi-frame depth fusion, so that the geometric position generated by the Gaussian point cloud is more accurate, and the subsequent optimization time is reduced.

The invention relates to the technical field of unmanned aerial vehicle cable intelligent routing inspection, and discloses a GPS-free unmanned aerial vehicle cable autonomous routing inspection method based on laser radar SLAM, and the method comprises the steps: completing the initialization setting of a system through carrying a laser radar, an inertial measurement unit and an auxiliary sensor; starting a laser radar SLAM (Simultaneous Localization and Mapping) algorithm in an environment without GPS (Global Positioning System) signals to carry out real-time positioning and map construction, and generating an environment point cloud map; planning an autonomous inspection path and optimizing a flight path based on the point cloud map and the preset geographic information of the cable line; controlling the unmanned aerial vehicle to fly along the path, and updating the position in real time by using an SLAM algorithm to compensate the positioning error; synchronously acquiring laser point cloud data, visible light images and infrared thermal imaging data of the cable; identifying a cable structure and an abnormal state through multi-modal data fusion processing; and finally generating an inspection report containing the health condition and the abnormal position of the cable and transmitting the inspection report to a monitoring center. The continuity and coverage integrity of the routing inspection mileage are improved.

The invention discloses an SLAM (Simultaneous Localization and Mapping) method and system based on visual inertial guidance and laser cascade registration, and belongs to the technical field of environmental perception, and the method comprises the following steps: acquiring multi-source data, predicting a pose by adopting an IMU (Inertial Measurement Unit) pre-integration method, taking the pose as an initial value, and outputting a robot visual-inertial pose by combining a camera image and utilizing a visual-inertial odometer technology; constructing a visual inertia factor; taking the vision-inertia pose as an initial value of registration, performing multi-stage registration on the input laser radar point cloud frame, outputting a laser estimation pose, and constructing a laser factor; visual loopback detection and laser loopback detection are respectively carried out, an effective closed loop is determined according to the space-time consistency of the two kinds of loopback detection, loopback factors are constructed, a factor graph is constructed, graph optimization is executed, and globally consistent tracks and map points are obtained. According to the method, the real-time performance is maintained, the registration convergence is improved, and the method is suitable for positioning and mapping in a complex degraded scene.

The invention belongs to the field of multi-sensor fusion systems, and discloses a multi-sensor fusion system simultaneous positioning and mapping method and a related device, and the method comprises the steps: obtaining an environment image, a laser point cloud and IMU data; scene semantic information is extracted through a multi-modal large model; dynamically generating a weight matrix of vision and laser radar relative to an IMU (Inertial Measurement Unit) by utilizing a vision and point cloud condition deep neural network in combination with scene information and IMU observation; and performing local and global optimization by combining each sensor speedometer factor, an IMU pre-integration factor and the weight matrix, and finally outputting robot state estimation and map point coordinates. Through semantic understanding and a self-adaptive weight mechanism driven by a conditional deep neural network model, a fusion strategy can be intelligently adjusted before environment change or sensor degradation, the robustness, precision and consistency of a system in an unstructured scene are improved, and the problems of positioning drift and map failure caused by fixed weight in a traditional method are solved.

The utility model discloses an antenna technical parameter measurement device and synchronous positioning measurement equipment relates to antenna measurement technical field. The device includes double satellite navigation module, including first satellite navigation unit and second satellite navigation unit, is used for receiving satellite signal and measures the heading angle, inertia measurement unit is used for measuring the pitch angle and the roll angle, microcontrol unit is connected with double satellite navigation module and inertia measurement unit respectively, is used for fusing heading angle, pitch angle and roll angle generation three -dimensional attitude data, communication module is connected with second satellite navigation unit, is used for obtaining real -time dynamic difference positioning information. The device through the multi -source data fusion of double satellite navigation unit and inertia measurement unit has realized high -precision three -dimensional attitude measurement, and combines real -time dynamic difference positioning technology simultaneously, has improved positioning precision and the reliability of attitude measurement significantly, is applicable to the application scene that needs accurate space attitude perception.

A method and a controller for controlling a vehicle and a vehicle including the controller. The method comprises: determining a navigation mode of a vehicle from a navigation mode group comprising a magnetic navigation mode and a simultaneous localization and mapping (SLAM) navigation mode based on preset information associated with a target path (301); determining a deviation between the position of the vehicle and the target path based on sensing information from a sensing device corresponding to the determined navigation mode (302); and controlling movement of the vehicle based on the determined deviation (303). According to the method, the flexibility and accuracy of navigation for vehicles (such as AGVs) can be effectively improved with low cost and workload.

Improved method for simultaneous localization and mapping; associated computer system and program. This method, performed by a computer (40) mounted on board a vehicle (1), consists of: acquiring a set of points at the current time, provided by a Doppler radar system (30) of the vehicle; acquiring the attitude at the current time of the vehicle, provided by an inertial measurement unit (20) of the vehicle; orienting the set of points at the current time with respect to a ground reference frame (X0Y0Z0), taking into account the attitude at the current time; processing the radial velocities of the points to calculate an estimated velocity at the current time of the vehicle (1); calculating a position at the current time of the vehicle (1) from the estimated velocity at the current time; and executing a simultaneous localization and mapping algorithm from the position at the current time of the vehicle over a plurality of successive times. Figure for the abstract: Figure 1

A method for simultaneous localization and mapping (SLAM) employs dual event-based cameras. Event streams from the cameras are processed by an image processing system to stereoscopically detect surface points in an environment, dynamically compute pose of a camera as it moves, and concurrently update a map of the environment. A gradient descent based optimization may be utilized to update the pose for each event or for each small batch of events.

The application belongs to the technical field of simultaneous localization and mapping, and particularly relates to a SLAM algorithm based on feature reinforcement and motion judgment in a dynamic scene, which applies a feature reinforcement instance segmentation network FENET and comprises the following steps: step S1, collecting image information and realizing feature recovery of a dynamic fuzzy object through a fuzzy feature recovery module; step S2, guiding a model to focus on key features of an object based on a reinforced feature recognition mechanism, and recognizing potential dynamic objects; and step S3, jointly estimating the pose of a camera itself and judging the motion of an object to remove a dynamic object. The application can reconstruct and recover lost feature information from a fuzzy image, greatly improves the recognition accuracy of a system for a dynamic object, greatly improves the recognition accuracy of a dynamic object, and avoids misjudgment of static features.

The invention discloses a household robot article searching method and system based on a multi-source pre-built map, and the method comprises the following steps: S1, controlling a household robot to move in a household scene through employing an autonomous synchronous positioning and mapping technology, obtaining environment data obtained by a multi-source sensor carried by the household robot, and storing the environment data in a database; constructing an environment map fusing the ground occupation information and the overlook visual information, wherein the environment map is used as a multi-source pre-established map for subsequent article search; s2, calculating three types of scores of each landmark in the home scene; calculating the comprehensive score of each landmark according to the three types of scores of each landmark, and selecting the first K landmarks with the highest comprehensive score to construct an associated landmark set; and S3, in combination with the multi-source pre-established map and the associated landmark set, constructing a high-low layer collaborative target exploration mechanism, and realizing layer-by-layer reasoning from region planning to action execution. According to the method, the semantic reasoning result is accurately sent to the ground, the actual navigation behavior is driven, and the accuracy of target positioning and the efficiency of path planning are improved.

This invention discloses a method, apparatus, and device for simultaneous localization and mapping (SLAM) of a mobile robot. The method involves acquiring depth information, LiDAR scanning information, and odometry information of the robot's environment; performing 3D projection on sparse point cloud data to obtain 2D information of the robot's environment; performing spatiotemporal synchronization processing on the 2D information, LiDAR scanning information, and odometry information; establishing pose nodes using the spatiotemporally synchronized 2D information, LiDAR scanning information, and odometry information; performing point cloud registration on the spatiotemporally synchronized 2D information and LiDAR scanning information using an iterative nearest point method; optimizing the pose nodes in real time using a second iterative nearest point method; and drawing a map based on the optimized pose nodes and pose transformation matrix; and completing loop closure screening using a third iterative nearest point method, with the selected loops used for loop closure detection. This invention can simultaneously leverage the advantages of LiDAR and visual sensors in unknown and complex environments.

The invention discloses an object-level semantic SLAM (Simultaneous Localization and Mapping) construction method and device based on NeRF. The method comprises the following steps: acquiring image data and camera parameter data; preprocessing the image data and the camera parameter data to obtain image preprocessing data and camera calibration parameter data; and utilizing an object-level semantic SLAM modeling model to process the image preprocessing data and the camera calibration parameter data to obtain camera pose data, object scene modeling data and scene object semantic segmentation data. According to the method, the NeRF technology is used for object modeling, and the problem that traditional object-level semantic SLAM object modeling precision is poor is solved; object semantic recognition is carried out by using an image segmentation large model SEEM, and the problem that a traditional object-level semantic SLAM cannot recognize objects except prior information is solved.

The embodiment of the invention provides an SLAM (Simultaneous Localization and Mapping) positioning method and device based on timestamp correction, equipment and a storage medium. The method comprises the following steps: acquiring a time compensation value, an image observation result and a system state of each window in a current sliding window; constructing an SLAM model according to the time compensation value of the window in the current sliding window, the image observation result and the system state, wherein the time compensation value is the time deviation between the time system of the camera and the time system of the IMU; and the SLAM model is solved to obtain to-be-estimated parameters, and the to-be-estimated parameters comprise the system state of each window in the current sliding window and a to-be-estimated time compensation value. In the method, the time compensation value and the system state are modeled together, so that continuous iterative optimization can be performed on the time compensation value, the time compensation value is used to compensate the time system of the camera, and the compensated time system of the camera is used to perform SLAM model modeling, so that the estimation result of the SLAM system is more accurate.

The application discloses a kind of indoor mobile robot dense mapping and autonomous navigation method based on depth camera, belong to robot simultaneous localization and mapping, robot navigation field.The application uses depth camera and based on interframe matching to image ORB feature to realize real-time positioning to robot, by fusing color image and depth image, and in order to eliminate redundant video frame, key frame extraction method in space domain is introduced, real-time dense three-dimensional point cloud map construction is realized, and it is converted into octree map format and grid map format suitable for navigation, then by ROS Navigation function package, it is combined with existing mobile robot navigation method, realizes indoor mobile robot autonomous navigation scheme based on pure vision scheme.

The invention discloses a semantic 3D Gaussian sputtering SLAM (Simultaneous Localization and Mapping) method based on illumination invariance. The method comprises the following steps: step 1, performing 3D Gaussian-based scene representation and joint micro-rendering on a to-be-constructed target area to obtain a multi-dimensional rendered initial scene model; 2, an internal appearance normalization IAN module is constructed, and decoupling of internal attributes of an initial scene model and transient illumination is achieved; 3, performing offset correction on a radiation field of a rendering graph in the initial scene model, and designing DRB-Loss loss to dynamically adjust correction intensity; step 4, through camera tracking optimization, combining a stable map and exposure compensation to realize robust pose estimation, and through double-stage key frame screening, improving SLAM system efficiency; step 5, constructing joint map loss, and optimizing all parameters of the 3D Gaussian primitive; and map updating is completed, invalid 3D Gaussian primitives are pruned, and a complete synchronous positioning and map building cycle is formed.

The application provides an automatic parking method, comprising the following steps: a domain controller receives an externally input parking instruction; the domain controller acquires 4D point cloud data, image data and positioning data according to the parking instruction; the domain controller performs fusion processing on the 4D point cloud data, the image data and the positioning data to obtain environmental data of a surrounding area of an automatic driving vehicle; the domain controller performs judgment according to the environmental data and a preset judgment condition; when the domain controller determines that the environmental data meets the preset judgment condition, target parking space data is determined according to the environmental data; the domain controller calls a preset simultaneous localization and mapping (SLAM) algorithm to perform route planning processing on the 4D point cloud data, the image data, the positioning data and the target parking space data to obtain parking route planning data; and the domain controller controls the vehicle to enter a parking space corresponding to the target parking space data according to the parking route planning data.

The invention discloses an SLAM (Simultaneous Localization and Mapping) positioning method and system based on probability intensity weighted ICP (Inductively Coupled Plasma). According to the method, modeling is carried out on an environment map through a random finite set theory, pose estimation is carried out by adopting a probability intensity weighted ICP algorithm, landmark existence probability and space uncertainty are introduced in registration to carry out adaptive weighting, and interference of clutter and leak detection is effectively inhibited. And at the same time, a Gaussian mixture PHD filter is combined to carry out closed updating on the map. According to the method, the problems of positioning drift and map distortion caused by data association errors of a traditional ICP algorithm in a complex noise environment are solved, and the precision, robustness and real-time performance of an SLAM system are remarkably improved.

The application discloses a target tracking method and system adapting to scenes and cascading data, and related equipment, and relates to the field of automatic driving perception. The method comprises the following steps: collecting and synchronizing a laser radar, a millimeter wave radar, vehicle state data and a simultaneous localization and mapping pose; predicting a target motion state by using an extended Kalman filter, wherein the process noise and the observation noise matrix can be dynamically adjusted according to the degree of vehicle motion and the target blocking condition; performing data association on the predicted trajectory and the detected target by using a three-level cascading strategy, and decoupling the height and depth errors step by step; finally, updating the trajectory state according to the association result, performing whole life cycle management, and outputting a tracking result. The application can solve the problems of low tracking accuracy, unstable trajectory and high missing matching rate of traditional methods in the conditions of vehicle violent maneuvering, target blocking, road bumping and long-distance scenes, and significantly improves the tracking robustness and state estimation accuracy in complex dynamic environments.

Embodiments of the present invention relate to techniques for managing keyframe data in a Simultaneous Localization and Mapping (SLAM) system of an Augmented Reality (AR) device. The method involves obtaining a plurality of keyframes, each linked to pose data comprising spatial and orientation data derived from raw data captured by sensors. The pose data for each keyframe is quantized according to predefined parameters, creating a structured pose grid of quantized cells. The technique includes analyzing the quantized pose data to identify excess keyframes that exceed a predetermined threshold within these cells. Redundant keyframes are pruned from memory, optimizing the SLAM system's efficiency by reducing computational load and memory usage. This selective pruning process ensures that the AR device retains a comprehensive and accurate environmental map while operating within the constraints of limited system resources.

The invention relates to the technical field of computer vision, and provides a synchronous positioning and mapping method for a robot under a corn field canopy, and the method comprises the steps: obtaining and preprocessing an environment image, IMU data and encoder data, and obtaining visual data, inter-frame motion increment and motion data; updating a world coordinate system state value based on the inter-frame motion increment; performing visual SFM processing on the basis of the visual data to obtain an image acquisition posture and a road sign point position; performing visual inertia combination to obtain an InEKF initial state; performing InEKF fusion based on the state value of the world coordinate system, the motion data and the initial state of the InEKF to construct a self-sensing odometer; and performing nonlinear optimization processing based on the visual data, the inter-frame motion increment, the motion data and the self-sensing speedometer to obtain a pose estimation result and a map point cloud. According to the method, feature matching, edge feature supplementation and IMU-encoder sensor fusion technologies are utilized, and high-robustness and high-precision robot positioning and map construction adaptive to the whole corn growth cycle are achieved.

The invention discloses a dense map construction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a map point cloud data list which comprises pose information and depth map information corresponding to at least one data frame, the pose information corresponding to the data frame is obtained from a simultaneous localization and mapping (SLAM) system; if loopback correction occurs in the SLAM system, pose information of corresponding data frames in the map point cloud data list is updated, and an updated map point cloud data list is obtained; obtaining target point cloud data based on the depth map information of each data frame and the pose information of each data frame in the updated map point cloud data list; the dense map is constructed based on the target point cloud data, so that the computing power burden of constructing the dense map based on the visual SLAM algorithm is reduced, and the accuracy of the constructed dense map is ensured.

The invention discloses a body-equipped robot positioning method, a body-equipped robot, equipment and a storage medium, and relates to the technical field of robot positioning, and the method comprises the steps: obtaining robot state data corresponding to the body-equipped robot; space-time registration is carried out on the robot state data, robot registration state data are obtained, and the robot registration state data comprise ultra-wideband collection data, synchronous positioning and map building data and visual data; determining a dynamic fusion weight corresponding to each data in the robot registration state data, and based on each dynamic fusion weight, carrying out feature fusion positioning on the ultra-wideband acquisition data, the synchronous positioning and map construction data and the visual data, and generating a real-time positioning result of the robot with the body in the robot body coordinate system. The positioning accuracy of the robot with the body can be improved.

The invention discloses a multi-sensor fusion SLAM (Simultaneous Localization and Mapping) data generation method and device, SLAM equipment and electronic equipment. The method comprises the following steps: constructing a first pose error according to a deviation between a gravity vector and an accelerometer measurement value under a local coordinate system, calculating first pose compensation data, and superposing the first pose compensation data with first inertial data to obtain compensated second inertial data; updating a pose based on the second inertial data, and obtaining pose data of the first environment image frame sequence under the global coordinate system; and calculating an optimized pose based on the first pose data and / or the second pose data, and associating the optimized pose with the global pose of the image frame to generate a pose constraint factor. Performing loopback detection on the second environment image frame sequence to obtain a loopback constraint factor; and carrying out joint modeling on the visual odometer factor, the laser odometer factor, the inertial measurement unit pre-integration factor, the image frame global pose and the loop constraint factor, and executing nonlinear optimization to generate SLAM data. Geometric consistency verification is carried out on visual feature matching through compensated inertial data, so that the reliability and precision of the front-end visual odometer can be improved; and visual loopback detection and laser loopback detection based on scanning context descriptors are complementarily fused, so that the robustness and the constraint reliability of loopback retrieval are improved, and the positioning and mapping precision and the global consistency of the SLAM system in a complex environment are remarkably improved.