67 results about "Loop closure" patented technology

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.

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.

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).

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.

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.

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 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.

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 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.

This application discloses a loop closure detection method, device, and storage medium. The loop closure detection method includes: selecting historical data frames from a historical data frame set whose absolute pose is adjacent to that of the current data frame to obtain candidate data frames; if the range difference between the sensing range of the candidate data frame and the sensing range of the current data frame is in a high difference threshold range, then selecting historical data frames from the historical data frame set whose acquisition time is adjacent to that of the candidate data frame and whose range difference between the sensing range of the candidate data frame and the sensing range of the current data frame is in a low difference threshold range to obtain supplementary data frames; constructing a historical local map by combining the candidate data frames and the supplementary data frames; registering the current data frame with the historical local map; and determining whether a loop closure occurs based on the registration result. This method can increase the number of historical data frames with more similar features to the current data frame, thereby improving the accuracy of loop closure detection.

The invention provides a positioning method, an electronic terminal and a computer readable storage medium, and the method comprises the steps: obtaining first point cloud data, and carrying out the compensation of the first point cloud data, and obtaining second point cloud data; matching the second point cloud data with a raster map to obtain a first matching result; and determining the pose of the target object based on the first matching result. According to the positioning method, the acquired first point cloud data is compensated, and the compensated second point cloud data is matched with the grid map in the loopback detection, so that the pose of the target object is determined, and the positioning precision of the target object in the loopback detection can be improved.

The invention relates to the field of computer vision, in particular to a pose closed-loop calibration method and system based on prism type screen splicing, and the method comprises the steps: collecting the relative pose data of a prism and each screen in real time, and synchronously obtaining the image data of a screen splicing region; calculating a picture distortion parameter of the image data according to the image data of the screen splicing area; if the picture distortion parameter exceeds a preset distortion threshold value, judging that the picture is abnormal; based on a judgment result, inputting the relative pose data and the picture distortion parameters into a preset error mapping model, determining a coupling error value of pose distortion through the error mapping model, and outputting a determined result; according to the coupling error value, analyzing a pose adjustment parameter of each screen; and based on the pose adjustment parameters of each screen, driving a pose adjustment mechanism installed on the display screen to compensate the relative position and angle of the prism and the screen.

This application relates to the field of computer vision technology and discloses a method and system for detecting pain expressions. The method includes: acquiring a facial video stream, segmenting muscle regions and extracting texture features after facial key point localization and inter-frame alignment; calculating motion energy based on these features, extracting enhanced micro-expression temporal segments and multi-scale spatiotemporal features, constructing a muscle dynamics model and completing state estimation; generating an adaptive candidate spatiotemporal window set through spatiotemporal attention weighted fusion; performing temporal modeling for dynamic feature fusion, and selecting the optimal window by fusing micro-expression features; and outputting the final pain level after double consistency verification and loop closure optimization. This application overcomes the limitations of static images, accurately captures facial dynamics and temporal changes, strengthens feature correlation, and significantly improves detection accuracy.

The invention belongs to the technical field of visual SLAM, and particularly relates to a visual SLAM method based on voxel grids and NeRF, and the method comprises the following steps: S1, carrying out the front-end tracking, and solving the posture transformation between images; s2, dynamic local mapping; s3, loopback detection is carried out; s4, designing a scene expression mode; s5, carrying out light ray sampling and volume rendering; the network architecture of front-end tracking in the step S1 comprises an optical flow estimation module and a neural network used for estimating the optical flow, and the neural network is composed of an encoder-decoder convolutional neural network and then used for adaptive camera attitude estimation; a camera pose coarse matching module initializes a pose estimation network using continuous images as input supervised training. According to the method, a micro chiral constraint attitude learning front-end method is combined with a neural radiation field, so that the generalization ability to an unfamiliar environment is relatively high, and the robustness is guaranteed; and the application effect in a complex environment is better.

This invention proposes a firefighting robot control method based on laser SLAM. The robot first uses Cartographer's SLAM algorithm to accurately extract image features from the disaster site, continuously building and refining the disaster site model based on these features. After receiving a firefighting mission, the robot first identifies flames based on information uploaded by its onboard depth camera. Once the flame source is identified, it actively plans a rescue route to reach the fire area as quickly as possible. Simultaneously, it uses its onboard binocular camera and LiDAR to avoid obstacles along the way, while infrared and distance sensors accurately identify the fire source, ensuring the robot quickly understands the situation on-site and can begin firefighting immediately upon arrival. This algorithm is based on an improved SLAM algorithm by Cartographer, incorporating a Lazy Decision algorithm in the loop closure detection part to optimize the Cartographer algorithm, effectively avoiding the consequences of loop closure errors and preventing incorrect loop closures.

The invention discloses a visual inertial positioning method, system and equipment for substation inspection and a storage medium. The method comprises the following steps: synchronously acquiring image data and inertial measurement unit data in a substation inspection process; extracting point features and line features from the current frame of image, and matching the point features and the line features with the previous frame of image to obtain a point and line feature corresponding relation; performing pre-integration on the data of the inertial measurement unit, and judging whether a new key frame is inserted or not by combining a visual observation result; constructing a joint optimization objective function in the sliding window, performing robust weighting on each residual item, and solving current state estimation through nonlinear optimization; and carrying out loopback detection by using a comprehensive matching result of the point features and the line features, and correcting the state quantity in the current sliding window to realize high-precision positioning. According to the method, the positioning robustness in a weak texture environment can be enhanced, abnormal values are inhibited, the anti-interference capability is improved, and the map consistency and reliability of long-term routing inspection are ensured.

The invention relates to a slam loopback detection method based on vision and laser line feature fusion. The method comprises the following steps: S1, extracting three-dimensional line features from laser radar point cloud data; s2, extracting two-dimensional line features from the visual image, and constructing an image line feature dictionary; s3, projecting the three-dimensional laser line features extracted in S1 to an image, and performing angle difference and Hausdorff distance-based matching with line segments in an image line feature dictionary to generate fusion line features; s4, in a loopback detection stage, through geometric error minimization and semantic consistency verification, determining that loopback of the current frame and the historical key frame is closed; and meanwhile, the fusion line features are added into the pose image in real time to optimize the current pose. According to the invention, the precision, robustness and long-term stability of the SLAM system in the mine environment are improved, and reliable technical support is provided for unmanned transportation of mines.

This invention belongs to the field of target detection technology, specifically relating to a multimodal feature fusion method for water target detection. The method includes the following steps in sequence: a preprocessing step, including: based on two adjacent RGB image frames as input, separating mirror perturbations to generate a mirror mask and a motion cue map; an alignment and fusion step, including: using a stable frame as the primary modality and the mirror mask and motion cue map as secondary modalities, extracting primary and secondary modal features; and inputting the fused features into a detection head to obtain initial candidate boxes and category scores; and a loop closure determination step, including: based on the conformal saliency ratio calculated for the initial candidate boxes and calibrated within the same frame, outputting the final detection result for that frame. This invention can automatically identify and eliminate unstable candidates caused by reflection. It also enhances the robustness and consistency of the model in complex scenes such as strong reflections, water wave disturbances, and changes in viewing angle.

This invention discloses a laser SLAM loop closure detection method based on gridded uniform sampling and multi-resolution registration, comprising: discretizing and constraining historical keyframes through spatial gridding organization and structural feature extraction to generate a first candidate loop closure set with structural consistency; performing initial pose correction on candidate loops in the first candidate loop closure set based on multi-source pose information, and performing stepwise optimization matching of candidate loops in combination with multi-scale point cloud representation to obtain a second candidate loop closure set; constructing a loop closure quality assessment model based on the point cloud matching results of candidate loops in the second candidate loop closure set, and filtering candidate loops through spatial distance-based matching score calculation and adaptive threshold determination mechanism, combined with geometric consistency verification, to obtain a final loop closure set.

This invention discloses a multi-sensor fusion SLAM method for dynamic scenes, belonging to the field of robot perception. The method includes the following steps: preprocessing visual, laser, and IMU data, including time and spatial synchronization; establishing sensor mathematical models and simultaneously constructing a visual-inertial subsystem and a laser-inertial subsystem, which are fused in a tightly coupled manner. The laser-inertial subsystem assists the visual-inertial subsystem in scale recovery, and the visual-inertial subsystem assists the laser-inertial subsystem in loop closure detection. Finally, a globally nonlinear optimization factor graph model is established through graph optimization, adding laser odometry factors, IMU pre-integration factors, visual factors, and loop closure detection factors to the factor graph to obtain globally optimal pose estimation and a 3D map. In dynamic environments, this invention reduces the impact of dynamic objects on localization by fusing information from multiple sensors, resulting in higher localization accuracy and robustness.

The invention discloses a loopback detection method, apparatus and device, and a readable storage medium. The method comprises the steps of obtaining a current pose of a current frame; screening a key frame having a loopback possibility with the current frame from each historical frame, and obtaining a key point cloud of the key frame; based on the key point cloud and the current pose, determining the number of spatial points used for representing the spatial relationship between the current pose and the key point cloud, and judging whether the number of the spatial points exceeds a preset number threshold; identifying whether the position of the current frame is matched with the spatial intervisibility relationship of the key frame by adopting a three-dimensional ray tracing method; determining that the current frame and the key frame form a loopback pair if the current frame exceeds the preset threshold value and the matching is successful; and if not exceeding and / or matching fails, determining that the current frame and the key frame cannot form a loopback pair. Therefore, loopback detection can be carried out by integrating the spatial overlapping degree and the spatial intervisibility relationship consistency, and the accuracy and efficiency of loopback detection are improved.