88 results about "Loop closure" patented technology

The invention discloses a loopback SLAM (Simultaneous Localization and Mapping) method based on three-dimensional Gaussian sputtering and multi-camera input. According to the invention, fusion of three-dimensional Gaussian sputtering and multi-camera input is utilized to realize autonomous panoramic data acquisition and scene reconstruction with higher acquisition efficiency; according to the method, the constraint is constructed by using the overlapped part between the cameras, so that more accurate camera pose estimation and high-quality scene modeling are realized; according to the method, timestamp attributes are added to gauss, the gauss are classified according to the timestamp attributes, and the loopback is rapidly and effectively detected through different classes of gauss proportions under a current frame camera view angle; after the loopback is detected, the camera pose is adjusted, the problem of camera drifting is solved, meanwhile, a Gaussian map can be updated according to adjustment of the camera pose, and an accurate three-dimensional model is kept. According to the two-stage binding adjustment strategy provided by the invention, the global camera pose is finely adjusted by using the multi-view rendering image loss and the pose image constraint.

The invention provides an automatic workflow code generation method driven by a large model, which comprises the following steps: sequentially converting an original demand into a subtask set, a node relation graph and structured workflow description data through multi-stage agent cooperation, and forcibly checking a data closed loop between nodes by the last-stage agent; tool nodes and content generation nodes are defined in the workflow description data, and decoupling of content generation and tool calling is achieved; analyzing the structured workflow description data, dynamically generating a strong type state class mapped with the description data, and automatically filling a node calling logic and a parallel routing rule based on a preset code template to generate a workflow code; when the code runs wrongly and the number of retry times exceeds a preset threshold value, an execution track is generated through the error positioning agent, candidate schemes are generated in parallel through the multiple repairing agents, and after self-adaptive scoring is conducted through the scoring agent based on the error type, the repairing schemes are verified and executed in sequence.

A forestry vehicle includes a number of different sensors, including LIDAR. A two-stage SLAM technique is used in combination with landmark trees to generate a tree map. This SLAM technique avoids large loop closures by resolving point cloud data into an arrangement of landmarks, reducing the computational working set to support real-time operations. A variety of services and visualizations are supported by the tree map.

The invention relates to a mapping system and method for flight inspection of indoor equipment, the system comprises a data acquisition end and an offline data processing end, the data acquisition end comprises an unmanned aerial vehicle and a sensing module, and the sensing module comprises a laser radar module, a binocular camera module, an inertial measurement module and an airborne recording module; the method comprises the following steps: synchronously acquiring multi-source data through layered flight; performing space-time alignment and preprocessing; self-adaptive sampling is carried out based on point cloud geometric prior guide image features, and laser-vision joint features are generated; fusing point cloud registration, joint features and inertial data, and carrying out joint estimation on adjacent frame pose increments; performing loopback detection based on the key frame to generate a closed-loop constraint; constructing and optimizing a pose map to eliminate cumulative drift; and finally fusing to generate a global point cloud map. According to the method, the space consistency and geometric accuracy of the map can be effectively improved, and a reliable data basis is provided for digital modeling and intelligent operation and maintenance of indoor equipment inspection.

The invention discloses a loopback detection method and device based on a tunnel inspection object and electronic equipment, and relates to the field of intelligent inspection. The method comprises the following steps: determining current pose data of a tunnel inspection object at a current time point based on sensor data, and when the tunnel inspection object establishes a current pose node based on the current pose data, determining a target pose node to construct a loop with the current pose node, judging whether a loopback constraint between the current pose node and the target pose node is constructed or not, and determining the similarity between the current image feature of the current time point and the image feature of the target time point indicated by the target pose node; and when it is determined that the loopback constraint between the current pose node and the target pose node is constructed and the similarity is greater than a preset similarity threshold, loopback detection is performed based on the loopback constraint. According to the method and the device, the technical problem of relatively low accuracy of loopback detection caused by easy establishment of wrong loopback constraints in related technologies is solved.

The invention relates to a method for determining a trajectory (330) for a mobile device, in particular a vehicle and / or robot that moves at least partially automatically, preferably a robotic lawnmower, especially to achieve better localization by enabling the mobile device to perform additional loop closures, wherein the mobile device is to move along the trajectory in a working area, comprising: determining the trajectory (330) for the working area, wherein trajectory segments (331a-331f, 332a-332g) are connected to form the trajectory, wherein at least some of the trajectory segments are determined such that at least one, preferably several, overlaps (334, 434) are present, in each of which a trajectory segment crosses another trajectory segment and / or runs sectionally at less than a predetermined distance therefrom, in particular superimposed,and providing the trajectory for use by a control or regulation unit for a drive unit of the mobile device.

This invention provides a laser SLAM loop closure detection method for unstructured orchards, comprising: acquiring point cloud data of a structured orchard and constructing a discriminative global representation of spatial binary patterns; calculating the column contribution of the spatial binary patterns and a low-dimensional attention score map with respect to row factors; constructing a KDTree based on the attention score map; searching for candidate loop closure frames and determining whether the overlap rate of the spatial binary patterns of the candidate frames and the current frame meets a threshold condition; if so, the loop closure detection is successful; otherwise, the loop closure detection fails. Experimental results using a harvesting robot in an actual orchard demonstrate the effectiveness of this method. Furthermore, experiments on the common outdoor dataset KITTI further prove the generalization ability of this method.

The present application discloses a map generation method and apparatus, a robot, and a storage medium. The method comprises: when local map statistical information in a storage device satisfies a loop closure detection condition, acquiring current point cloud data and storing same into a memory (step 202); in the memory, on the basis of a location distance that is calculated using location information contained in the current point cloud data and location information corresponding to each local map identifier, determining a target local map identifier from among the local map identifiers, extracting, from the storage device, a target local map corresponding to the target local map identifier, and storing the target local map into the memory (step 204); in the memory, on the basis of a pose error between pose information contained in the current point cloud data and pose information corresponding to the target local map identifier, performing pose correction on initial point cloud data corresponding to each local map identifier in the storage device to obtain target point cloud data corresponding to the local map identifier, and extracting each piece of target point cloud data from the storage device and storing the target point cloud data into the memory (step 206); and in the memory, respectively generating each updated local map on the basis of each piece of target point cloud data, replacing a historical local map corresponding to each local map identifier in the storage device with the corresponding updated local map, and on the basis of the updated local maps, generating a target global map (step 208).

The invention provides a camera pose correction method, a three-dimensional reconstruction method and a related device, and the method comprises the steps: obtaining an image from an image data set, and enabling the image and a current image to meet a preset common-view condition to serve as a common-view image; obtaining a relative pose between the camera shooting the current image and the camera shooting the common-view image as a common-view relative pose; acquiring an image meeting a preset similarity condition with the current image from the image data set, and if the image is not the same as the common-view image, taking the image as a loopback image; obtaining a relative pose between the camera shooting the current image and the camera shooting the loopback image as a loopback relative pose; and correcting the camera pose corresponding to each node in the pose image by taking the reduction of the difference between the loopback relative pose and the common-view relative pose as an optimization target and taking the minimum change of the relative pose of the edge of the pose image as a constraint term so as to eliminate accumulated errors. The method can improve the accuracy of the camera pose.

This invention discloses a ground-air collaborative mapping method based on semantic features and ground plane multi-constraint fusion. The method includes: generating sub-map sets from local point cloud data collected by fire trucks and drones; and generating rotation-invariant semantic descriptors using a maximum height value rasterization strategy and rotation group enhancement. By calculating geometric and semantic distances, and combining isomorphic and heterogeneous pattern weighted fusion, candidate sub-map pairs with loop closures are selected and geometrically registered to generate loop closure constraints. Simultaneously, ground point clouds are extracted using the RANSAC algorithm, and the ground plane model is smoothly updated using a sliding window to construct height and pose alignment constraints. The loop closure constraints, ground plane constraints, and pose information are input into the Ceres optimization framework for joint optimization to generate a globally consistent overall map, effectively improving the accuracy and robustness of ground-air collaborative mapping, and is suitable for emergency scenarios such as complex fire rescue.

Methods for loop closure detection performed by a processor of a robotic device may include maneuvering the robotic device through a plurality of locations, determining a location of the robotic device at each of the plurality of locations using a round trip time (RTT) measurement of a signal sent to an access point, determining whether a location of the robotic device is within a threshold distance of a previously-visited one of the plurality of locations, and performing visual loop closure detection in response to determining that the location of the robotic device is within the threshold distance of the previously-visited one of the plurality of locations.

The present disclosure provides a method, an apparatus and a system for detecting a loop closure in an environment, the method executed by a processor, and comprising: identifying a text entity comprising a text content in the environment from an image frame associated with an image sensor; estimating a pose of the text entity in a current local coordinate frame associated with a depth sensor based on an estimated pose of the text entity in the image frame and an extrinsic parameter between the depth and image sensors; retrieving from a database using the text content a candidate pose of the text entity in a previous local coordinate frame; calculating a relative pose constraint relating to a transformation between the current and previous local coordinate frames based on the pose and the candidate pose of the text entity; and detecting the loop closure based on the relative pose constraint.

The present disclosure provides a laser point cloud loop detection method and system based on a triangular pyramid local descriptor, which relates to the field of loop detection technology and is used to retrieve the loop frame of the current key frame from the key frame sequence. The method comprises: constructing a global descriptor and a triangular pyramid local descriptor for each key frame in the key frame sequence; based on the global descriptor, coarsely searching for a preliminary candidate loop frame set from the key frame sequence; based on the triangular pyramid local descriptor, finely searching for a final candidate loop frame set from the preliminary candidate loop frame set; filtering the loop frame of the current key frame from the final candidate loop frame set through geometric verification and validity judgment; wherein the triangular pyramid local descriptor performs density clustering on the point cloud of the key frame based on the number of points in the neighborhood, constructs a triangular pyramid using the clustering result, and encodes the information of the triangular pyramid as the triangular pyramid local descriptor of the key frame. The present invention has the advantages of high precision, strong robustness, and simple calculation.

The present invention belongs to the field of computer vision, and particularly relates to a SLAM loop closure detection and pose graph optimization method based on motion constraints, aiming to solve the problems that the SLAM loop closure detection and pose graph optimization technologies have a slow running speed, a low recall rate, and do not fully integrate kinematic knowledge, resulting in poor robustness of SLAM. The method of the present invention includes: determining whether the current frame image is a key frame, and if so, calculating the relative poses between key frames and constructing a pose graph; taking the N historical key frames with the smallest global binary feature distance between the current frame image and each historical key frame as loop closure candidate frames; determining whether the distances between each loop closure candidate frame and the current frame image are all greater than a set distance threshold, and if not, optimizing the pose graph, otherwise extracting the local features of each loop closure candidate frame for matching and loop closure detection, and if the loop closure detection is successful, optimizing the pose graph, otherwise re-acquiring the frame image. The present invention improves the robustness of simultaneous localization and mapping.

Examples described herein provide a method for trajectory estimation using an image sequence. The method includes receiving an image sequence of an environment from a camera moving relative to the environment. The method further includes extracting and matching features from images of the image sequence. The method further includes determining a relative orientation of the images of the image sequence. The method further includes determining orientation parameters of the camera using sequential image resection. The method further includes performing, using the orientation parameters, bundle adjustment to generate refined orientation parameters of the camera. The method further includes estimating a trajectory of the camera relative to the environment based at least in part on the refined orientation parameters by performing loop closure.

The present application relates to the technical field of computer vision, and discloses a loop closure detection method, an apparatus, a device, and a storage medium. The method comprises: determining a candidate key frame subset on the basis of an original image and a loop closure detection key frame database; when the candidate key frame subset is not empty, respectively determining a target image set for constructing a key frame and an original visual image; determining a matching point cloud pair by means of the target image set and the original visual image on the basis of a dense reconstruction thread, and calculating a similarity error of the matching point cloud pair; and performing loop closure detection on the basis of the similarity error. By means of said method, after a candidate key frame subset is determined, whether the candidate key frame subset is empty is determined; if so, it indicates that no loop closure is present, and if not, a dense reconstruction thread is created, and a valid matching point cloud pair is determined on the basis of the dense reconstruction thread; and then, loop closure detection is performed using an image pair corresponding to the similarity error, thereby effectively improving the accuracy of loop closure detection, improving SLAM tracking accuracy.

This application belongs to the field of robots, and provides a robot, a mapping method, a device and a storage medium thereof. The method includes: obtaining key frames, and determining the robot poses corresponding to the key frames; determining the poses of the positioning identifiers according to the images of the positioning identifiers in the key frames and in combination with the robot poses corresponding to the key frames; using the robot poses corresponding to the key frames and the poses of the positioning identifiers as nodes, and using the relative pose relationships between the key frames and the relative pose relationships between the positioning identifiers and the robot poses corresponding to the key frames as edges to generate a pose graph including loop closures; optimizing the robot poses corresponding to the key frames and the poses of the positioning identifiers according to a graph optimization method. Thereby, it can effectively improve the accuracy of the robot poses corresponding to the key frames and the poses of the positioning identifiers, which is beneficial for the robot to perform positioning and navigation more reliably and reduce the frequency of map reconstruction.

This invention discloses a dynamic environment SLAM localization method based on multi-source sensor data fusion, comprising the following steps: acquiring visual, inertial, and laser data and calibrating the buffer; performing HRNet segmentation and ORB-SLAM tracking on the visual data to obtain the initial pose; fusing the inertial and laser results to obtain the current pose; determining and recalculating the divergence of time window data to update the current pose; inserting keyframes and generating a semantic skeleton map; encoding and generating a semantic topology string and associating it with the keyframe index; retrieving loop closure candidate frames and performing geometric verification followed by map optimization to obtain the localization result and map result. This invention achieves highly robust localization and mapping in dynamic environments and improves the accuracy of loop closure recognition and map consistency.

The application discloses a kind of pose closed loop planning system and method based on PLC six visual quality inspection, it is related to industrial robot quality inspection technical field, including: visual perception module, for obtaining RGB-D image;Pose closed loop control module, for receiving RGB-D image and output 6D pose data;Motion planning module, for generating double-arm motion trajectory;Flip optimization module, for generating optimal flip sorting set;Sorting execution module, for completing sorting and discharging action according to quality inspection result and control instruction;Robot controller, for data interaction and issue control instruction.The application does not need to rely on artificial flip or multiple fixtures, reduce the failure rate caused by incoming material position deviation etc., through double-arm alternate operation and Monte Carlo optimization flip sorting, realize PLC device six automatic continuous detection, with the combination of coarse positioning, fine positioning of double RGB-D camera, combined with the output 6D pose data of deep learning pose estimation model, effectively avoid perspective distortion and miss detection, false detection.

The application relates to a loop detection method based on a point cloud semantic graph descriptor and position information, comprising the following steps: acquiring laser point cloud data and satellite positioning data of a current frame, performing semantic segmentation processing on the laser point cloud data, converting into a preset point cloud semantic graph descriptor form, screening a loop candidate frame from historical frame data through distance matching and node matching; converting the current frame and the loop candidate frame into a circular ring graph form, and attempting to obtain a target loop frame through similarity verification and geometric consistency verification. Compared with the prior art, the application solves the problems of difficult loop detection, large loop matching calculation amount and low loop matching accuracy in a mobile robot SLAM system, and improves the positioning accuracy and system robustness of the mobile robot.

The invention discloses a loop closing device topological structure deduction optimization method and a related device, and belongs to the technical field of power distribution network flexible loop closing, and the method comprises the steps: obtaining a power electronic and electromagnetic hybrid loop closing device through deduction based on a power electronic loop closing device and an electromagnetic loop closing device; and based on the obtained power electronic and electromagnetic hybrid loop closing device, reducing the capacity of power electronic equipment is taken as a core, and a topology scheme of the power electronic and electromagnetic hybrid loop closing device is obtained through optimization. According to the novel hybrid loop closing device topology scheme deduced by the method disclosed by the invention, the cost is reduced while the power flow regulation and control precision is ensured.

This invention relates to the fields of computer vision and robot localization technology, and provides a method, system, device, and medium for loop closure detection based on a panoramic semantic topology map. The method includes: first, inputting a panoramic image into a semantic segmentation and depth estimation network to obtain a semantic map and a depth map, respectively; removing dynamic objects; and then extracting the centroid coordinates of each static object. The depth value corresponding to each centroid coordinate is calculated based on the depth map, and the three-dimensional spatial coordinates of each static object are obtained through coordinate transformation. When the spatial distance between static objects is less than a threshold, edge connections are established to generate a panoramic semantic topology map. By calculating the similarity between semantic topology maps, it is determined whether the user has returned to a previously visited location, thus completing the loop closure detection task. This method integrates high-level image information such as semantics, depth, and spatial position relationships, reducing perceptual ambiguity and mitigating the impact of changes in viewpoint, lighting, and dynamic objects on loop closure detection.

This application provides a method, apparatus, electronic device, storage medium, and program product for robot map building. The method includes: constructing a map set based on environmental data collected during robot movement; constructing registration constraints for each sub-map based on the Euclidean symbolic distance field and surface voxel set of each sub-map; constructing odometry constraints for each sub-map based on the relative pose between any two adjacent sub-maps in the map set; performing loop closure detection on each sub-map based on loop closure information collected during robot movement, and constructing loop closure constraints for each sub-map; determining a reference pose map based on the loop closure information, and constructing global constraints for each sub-map based on the reference pose map; adjusting each sub-map based on the registration constraints, odometry constraints, loop closure constraints, and global constraints, and fusing the adjusted sub-maps to obtain a global map. This application improves the accuracy and consistency of map building with the environment.

This invention relates to the field of indoor positioning and mapping technology, specifically disclosing a SLAM method, apparatus, and system based on a multi-level spatial structure. The method includes: acquiring image information and image depth information at the same time; preprocessing the image information and the image depth information; performing geometric consistency verification on image keyframe information, and performing local optimization processing on the image keyframe information that has passed geometric consistency verification; performing loop closure detection based on the image keyframe information, and constructing corresponding principal direction constraints based on different loop closure detection results; performing global optimization on the locally optimized map information and locally optimized camera pose information based on the principal direction constraints to obtain globally optimized map information and globally optimized camera information; and constructing a map based on the globally optimized map information and globally optimized camera information. The SLAM method based on a multi-level spatial structure provided by this invention can improve positioning and mapping accuracy.

This invention relates to a biomimetic brain-like synchronous localization and environmental perception method for underwater robots. The invention addresses the problems of poor visual odometry, insufficient robustness, and low accuracy in loop closure detection in existing underwater robot navigation systems. The robot first acquires environmental feature information through sonar sensors and its own motion information through navigation sensors. A local scene template is obtained by processing the sonar data using an acoustic-visual processing method. The sensor data undergoes pre-integration processing, and the processed data is used as input to a convolutional neural network to output motion displacement, thus forming the robot's perception of its own position. Finally, an empirical map integrates the above information, and loop closure detection updates the empirical map, reducing drift during robot movement and completing the construction of the empirical map. This invention belongs to the fields of bionics and motion navigation technology.

An electronic device [100] tracks its motion in an environment while building a three-dimensional visual representation [225] of the environment that is used to correct drift in the tracked motion. A motion tracking module [210] estimates poses of the electronic device based on feature descriptors [215] corresponding to the visual appearance of spatial features of objects in the environment. A mapping module [220] builds a three-dimensional visual representation of the environment based on a stored plurality of maps [417], and feature descriptors and estimated device poses received from the motion tracking module. The localization module performs a loop closure by minimizing the discrepancies between matching feature descriptors to compute a localized pose. The localized pose corrects drift in the estimated pose generated by the motion tracking module.

The invention discloses a bag-of-word model loopback detection method based on a depth image, and the method specifically comprises the following steps: 1, carrying out the registration of a laser point cloud and a visual image, and calibrating the external parameters of a laser radar and a camera; 2, extracting linear edges of all object contours in the field of view, and converting all point cloud coordinates to a camera coordinate system by aligning edge features in a point cloud image of the laser radar and a visible light image of the camera to realize depth projection to obtain an initial depth image; 3, complementing the initial depth image; generating a complemented complete depth image; and step 4, for the complemented complete depth image, detecting a loop by using a bag-of-word model loop detection mechanism. The loopback detection precision is higher.

This invention relates to a laser SLAM method and system that integrates visual loop closure detection. The method includes: S1: acquiring laser point cloud data using a 3D LiDAR and constructing laser point cloud keyframes, and using these keyframes to obtain current positioning information; S2: registering the point cloud data contained in each laser point cloud keyframe to the world coordinate system to obtain a global point cloud map; S3: acquiring visual images using a visual camera and fusing the laser point cloud keyframes to construct point cloud-image fusion keyframes to detect loop closure information. When a loop closure is detected, the corresponding loop closure constraint and loop closure trajectory are obtained; S4: establishing a pose graph model based on all point cloud-image fusion keyframes on the loop closure trajectory, and optimizing the pose graph model using loop closure constraints as constraint edges to obtain optimized point cloud-image fusion keyframes to update the current positioning information and the global point cloud map. The method and system provided by this invention can improve the accuracy of positioning and mapping in laser SLAM systems.

The invention relates to a method for determining the trajectory of a mobile device, in particular an at least partially automatically moving vehicle and / or robot, preferably a mowing robot, in particular for better positioning in such a way that the mobile device can carry out an additional loop closure, wherein a mobile device should move along said trajectory in a working area, comprises: determining a trajectory (330) for the working area, in which trajectory segments (331 a-331 f, 332a-332 g) are connected into the trajectory, in which at least some of the trajectory segments are determined such that there is at least one, preferably a plurality of overlaps (334, 434), in which the trajectory segments (331 a-331 f, 332a-332 g) of the trajectory segments (331 a-331 f, 332a-332 g) of the trajectory segments (331 a-331 f, 332 a-332 g); wherein one trajectory section intersects the other trajectory section and / or extends, in particular overlaps, section by section at a distance less than a predetermined distance therefrom, and the trajectory is provided for a drive unit of the mobile device for use by an open-loop or closed-loop control unit.

The application discloses a loop closure detection method based on block feature uniform weighting and distance sorting in outdoor complex environment, and belongs to the technical field of visual camera simultaneous localization and mapping and computer vision. The technical scheme of the application comprises the following steps: performing gray processing and Gaussian filtering pretreatment on the collected image pair, uniformly dividing the image into a plurality of sub-image blocks, extracting local feature descriptors from each sub-image block, calculating the distance between the feature vectors of the corresponding sub-image blocks based on cosine similarity, uniformly weighting the feature similarity of all sub-image blocks, sorting the calculated feature distances of all sub-image blocks from small to large, screening out the key matching block with the smallest distance by setting a threshold to eliminate the false matching block caused by illumination change noise, and comprehensively determining the global similarity of the feature distance of the key matching block to confirm the success of loop closure detection and output loop closure information to optimize the global pose of the visual camera.