22 results about "3d positioning" patented technology

This invention relates to the field of orchard navigation technology, specifically a 3D method for orchard operations based on a grid map. The method includes: constructing an orchard point cloud map using a mapping algorithm; filtering the acquired 3D point cloud map and constructing a triangular grid map of the orchard, embedding the orchard's GPS information into the triangular grid map; based on the triangular grid map with GPS information, performing ICP registration between the point cloud data scanned by the LiDAR and the points on the triangular grid map using the RMCL 3D positioning algorithm, outputting the 3D pose of the operation robot in the orchard; updating the 3D pose of the operation robot in the orchard using GNSS information; planning the navigation path of the operation robot using grid navigation based on the GPS information of the target point; and controlling the operation robot to dynamically avoid obstacles and navigate to the target point. This invention enables precise positioning and autonomous navigation of the operation robot in hilly orchards, allowing it to complete tasks such as orchard inspection, plant protection, harvesting, and transportation.

This application discloses a multimodal data collaborative intelligent detection and 3D localization method for maritime vessels, comprising: fine preprocessing of collected multi-source heterogeneous data and preliminary target detection; dynamic weighted fusion of the preprocessed multi-source heterogeneous data, combined with depth data from lidar, to achieve accurate coordinate recovery of the vessel target in 3D space; and prediction and updating of the motion state of the identified target based on a multi-tracking mechanism collaborative strategy. By fully utilizing the rich texture information of visible light images, the anti-interference capability of thermal imaging, and the precise spatial depth data of lidar through multimodal data perception and preliminary processing, heterogeneous data deep fusion and 3D localization, and advanced multi-target continuous tracking and state estimation, this method integrates a multi-tracking mechanism collaborative strategy to predict and update the motion state of the identified target, effectively addressing the problem of tracking interruption caused by nonlinear motion or temporary occlusion of maritime targets.

This invention provides a 3D laser SLAM fusion magnetic stripe inspection robot navigation and positioning method, belonging to the field of navigation and positioning technology. The method includes: initializing the robot by acquiring an initial pose and presetting a rectangular boundary for a dynamically changing region; determining in real time whether the current pose is within the dynamically changing region; if the current pose is outside the dynamically changing region, registering the current frame of laser data with a pre-constructed point cloud map to obtain a first positioning pose; if the current pose is within the dynamically changing region, performing laser fusion magnetic stripe positioning: acquiring the initial 3D positioning pose and initial magnetic stripe data; determining the initial vertical deviation distance; acquiring the real-time 3D positioning pose and real-time magnetic stripe data; determining the real-time vertical deviation distance; and correcting the pose output by the laser odometry through forced constraints to determine the final fused pose. According to this invention, interference in dynamically changing regions can be better addressed, achieving stable and reliable navigation and positioning.

This invention provides a method, system, and medium for three-dimensional positioning and displacement measurement that integrates microwave and vision technologies. The method includes: rigidly connecting at least one microwave sensor and at least one vision sensor as a joint sensing unit; establishing a spatial transformation relationship between the coordinate systems of the two sensors through calibration; fusing the target's radial distance and angle information provided by the microwave sensor with the target's two-dimensional imaging information provided by the vision sensor; and directly solving for the target's three-dimensional coordinates in the selected coordinate system using an optimization algorithm; further combining the high-precision one-dimensional displacement information from the microwave sensor with the two-dimensional displacement information from the vision sensor to reconstruct the target's complete displacement vector in three-dimensional space. This invention, through a compact heterogeneous sensor fusion architecture, effectively solves the problems of limited dimensionality of single-sensor modes, high cost of multi-sensor networking, system complexity, and poor engineering applicability, achieving high-precision, low-cost, and full-dimensional non-contact three-dimensional positioning and displacement measurement.

This invention discloses a natural language interactive navigation, obstacle avoidance, and grasping method for a robotic dog based on a nine-grid large model. The method includes the following steps: Step S1: Parsing user commands to extract key information and publishing standardized ROS2 topics; Step S2: Processing radar point clouds and fusing multi-sensor data to establish a coordinate transformation chain; Step S3: Navigating and avoiding obstacles to the target location based on the SLAM and Nav2 framework; Step S4: Completing target recognition and precise 3D positioning using a wrist camera and YOLOv8; Step S5: Generating a grasping action sequence and achieving closed-loop control of the robotic arm through smooth trajectory planning. This invention integrates nine-grid large model semantic parsing and a hierarchical modular architecture with multiple core technologies to achieve natural language interactive driving for the robotic dog, as well as the coordinated completion of navigation, obstacle avoidance, and object grasping, significantly reducing the operational threshold and improving grasping accuracy and motion safety.

This invention discloses an integrated method for low-altitude UAV radio frequency positioning and intelligent trajectory control, relating to the cross-technical field of low-altitude UAV radio frequency positioning and trajectory control. The specific steps of this method are as follows: performing radio frequency synchronous calibration, with multiple ground base stations and an airborne radio frequency unit interacting to adjust parameters and output calibration datasets; positioning calculation and evaluation, outputting positioning results, evaluation data, and feedback interference characteristics; conducting trajectory feedforward control, generating and adjusting commands; optimizing flight control feedback, generating predicted values ​​to adjust algorithm parameters; and closed-loop iterative optimization, integrating parameters to adapt to low-altitude flight scenarios. This invention achieves dynamic adaptation of radio frequency signal parameters and acquisition strategies through collaborative calibration between multiple ground base stations and an airborne radio frequency unit, improving the accuracy and stability of UAV 3D positioning. Through integrated design of positioning calculation and trajectory control, a closed-loop iterative process is achieved, reducing system latency and errors, enhancing scenario adaptability, and ensuring operational safety and management efficiency.

This invention provides a method and apparatus for visual detection and harvesting control of guava maturity, relating to the field of visual detection technology. It acquires multi-view time-series images from a drone and extracts individual guava image blocks and depth information to determine spatial location. A feature encoder extracts feature vectors and performs clustering to determine maturity levels. Then, an optimization problem is constructed to plan the harvesting path, aiming to maximize harvesting revenue per unit time. Finally, the drone and robotic arm are controlled for flexible harvesting. This method can achieve automatic identification of guava maturity, 3D positioning, revenue-driven target selection and path planning, and precise flexible harvesting even in the absence of numerous manual maturity labels. It forms an integrated autonomous harvesting system with a closed loop of perception, understanding, decision-making, and execution, effectively solving problems such as difficult feature extraction, high manual labeling costs, low planning efficiency, and fruit damage in greenhouse guava harvesting.

This application relates to a navigation method and system for underground parking lots based on multi-source fusion. The method includes acquiring a 3D building information model of the underground parking lot and constructing a 3D navigation topology map based on the 3D building information model; collecting multi-source data, performing preprocessing operations on the multi-source data to obtain preliminary multi-source positioning results; evaluating the quality indicators of the preliminary multi-source positioning results to obtain multi-source positioning quality evaluation results, and performing dynamic weighted fusion calculations based on the quality evaluation results to obtain 3D positioning results representing the user's real-time location; and performing path planning based on the 3D positioning results and the user's target location, combined with the 3D navigation topology map, to obtain the target navigation path. This application improves the accuracy of navigation in underground parking lots.

A system and method are provided for providing continuous automatic alignment of bone(-pieces), instruments, and implants based on only one additional X-ray image without the need to change the position of the X-ray source. According to the invention, a 2D X-ray image is received, which shows the target surgical site. Artificial intelligence-assisted automatic detection and localization of objects such as bone(-pieces), tools (such as drills), and / or implants, combined with prior information about the surgical procedure, is used to calculate their relative 3D positions and orientations at any desired time. This alignment of objects can be repeated multiple times, thus providing guidance in quasi-real time.

A visual positioning method for an installation manipulator for a support arm of an overhead line network, characterized in that it comprises the following steps: Step 1: Capturing an image of an installation location using a depth camera and preprocessing the image; Step 2: Inputting the preprocessed image into an enhanced YOLOv8 network model for feature extraction and recognition; Step 3: Inputting the 2D image recognized in Step 2 into an image processing module, wherein the image processing module combines the depth information obtained from the depth camera to obtain 3D positioning data; Step 4: Transmitting the 3D positioning data obtained in Step 3 in real time to a manipulator control module, wherein the manipulator control module performs path planning to generate a motion trajectory and control instructions to direct the manipulator to execute a corresponding movement;Step 5: The image processing module continuously focuses on a target position during the movement of the manipulator, performs error detection, and adjusts the movement of the manipulator in real time; Step 6: The depth camera, the manipulator control module, and the image processing module send the on-site image and an installation situation back to a control terminal, and the operator monitors the on-site installation situation via the control terminal and intervenes manually via the control terminal;wherein the improved YOLOv8 network model comprises an input layer, a CSP multi-scale feature extraction module, an environment-adaptive feature extraction module, a multi-head self-attention mechanism, and a precise positioning optimization module, wherein the environment-adaptive feature extraction module is embedded within the CSP multi-scale feature extraction module, and wherein the image is output sequentially through the environment-adaptive feature extraction module, the multi-head self-attention mechanism, and the precise positioning optimization module;wherein an environment-aware unit of the environment-adaptive feature extraction module uses global average pooling and a full interconnect layer to extract global environmental information from an input feature map, and dynamically adapts a feature extraction strategy of the network in different environments, wherein the precise positioning optimization module improves the positioning accuracy of the support arm of the overhead line network through anchor point optimization, structure perception, and an improved positioning loss function; wherein a specific processing procedure of the environment-adaptive feature extraction module is as follows: after the input feature map is processed by the global average pooling and the full interconnect layer, the input feature map is transformed into an environment feature vector;Based on the environment feature vector, an original feature map is adaptively modulated using a specific formula as follows: e=sigmoid(W2⋅ReLU(W1⋅GAP(X)))X'=X□(1+γ⋅e) where W1 and W2 are a weight matrix of the full connection layer, where sigmoid is an activation function, where X is the input feature map, where e is the environment feature vector, where X' is a modulated feature map, and where γ is a learnable scaling factor, where □ denotes a channel-by-channel multiplication operation;wherein the feature maps modulated by the environmental feature vector are processed by dilation folding with different expansion rates, with a specific formula as follows: C=[Convd1(X'),Convd2(X'),Convd4(X')] where Convdx denotes the dilation folding operation with a 3*3 expansion rate of X, where d1, d2 and d4 each denote different expansion rates, where C represents a splicing result of the feature maps processed by dilation folding with three different expansion rates; wherein the multi-scale features are fused by 1*1 folding to produce a final feature map F.;

This invention proposes a visible light positioning method and system based on spatiotemporal and angular feature fusion, relating to the field of indoor positioning technology. The method includes: constructing a training dataset based on an indoor positioning scene, including visible light positioning pilot maps, coordinate labels, distance labels, and angle labels; inputting the training dataset into a positioning model for training, extracting preliminary features through a feature embedding module, and then capturing the temporal dependencies of the preliminary features and enhancing spatial perception through a feature fusion module to obtain fused features; inputting the fused features into position extraction branches and angle extraction branches at different depths to obtain position information and angle information respectively; calculating a loss function until the loss converges to obtain a trained positioning model; inputting the visible light positioning pilot map of the receiver to be positioned into the trained positioning model to obtain positioning information. This achieves high-precision and robust 3D positioning, effectively solving the problems of insufficient feature mining and poor scene adaptability in traditional VLP technology.

This invention provides a port long-channel fog navigation system and method based on UAV navigation and small target detection and localization, comprising: a guided vessel and a pilot terminal layer, an aerial UAV, and a maritime support platform; the guided vessel and pilot terminal layer includes: a guided merchant ship and a pilot tablet terminal; the aerial UAV includes: two UAVs operating in a forward formation navigation mode; the two UAVs respectively acquire images and point clouds at a fixed frequency, attach a unified timestamp and their own attitude information, and send them to a tugboat RTX edge computing base station, where the tugboat RTX base station performs data fusion; the maritime support platform includes: a tugboat RTX base station, which establishes a unified ENU coordinate system, transmits data through a dedicated wireless link and a data access gateway, fuses the observation data from the two UAVs, and performs 3D positioning and collision avoidance calculations. This invention significantly improves the detection accuracy of small targets and the ability to share 3D situational awareness, thereby reducing the risk of collision.

This invention discloses a pallet 3D positioning method and system based on forklift edge computing, relating to the field of intelligent warehousing technology. The invention includes the following steps: dividing the warehouse into multiple storage areas based on work processes; binding each pallet within each storage area with its corresponding feature information and the information of the items it contains; issuing target tasks to target forklifts from the cloud; the target task includes the feature information of the target pallet, which includes pallet feature information and environmental feature information; transferring the status of multiple target forklifts within the same storage area that meet preset trigger conditions; by setting a homomorphic graph, this invention enables multiple target forklifts within the same storage area to update the pallet-picking status in a timely manner, reducing the possibility of inaccurate pallet picking due to changes in the surrounding environmental features caused by delays.

This invention discloses a multimodal localization and grasping control method and system for a tomato harvesting robot, belonging to the field of intelligent control technology. It includes: real-time multimodal data processing; using a tomato target detection model based on a YOLOv4 deep learning network to recognize RGB images, outputting bounding box coordinates, confidence scores, and maturity categories; determining the tomato target to be grasped based on a multi-condition filtering mechanism, and calculating the three-dimensional spatial coordinates of the tomato target by fusing depth images and 3D point cloud data; constructing a real-time 3D map and a global environment map using the Octomap octree map construction algorithm to determine the target's spatial location, neighborhood distribution, and end effector approach direction information; and planning the robotic arm's motion path using a two-stage path planning method. This invention can effectively improve the accuracy of tomato target recognition and 3D positioning precision in complex agricultural environments, significantly increasing the success rate of harvesting operations.

This invention discloses a positioning method and device. The method includes: acquiring and preprocessing a depth map; removing noise points based on depth gradient smoothness analysis; and completing the object contour through gradient direction inference and interpolation; extracting precise single-pixel edges based on gradient features; converting the depth map into a point cloud and performing noise reduction correction; and introducing multi-objective optimization for grasping stability based on the traditional geometric center, upgrading the positioning center to an optimal grasping target point that takes into account contact area, center of gravity offset, surface flatness, and clamping force constraints, making it more suitable for high-precision and stable grasping of small objects without surface features. The entire process does not rely on the object's surface texture, grayscale, or contour features, but only on the geometric properties of the depth gradient. This invention achieves high-precision and robust 3D positioning of objects without surface features, effectively improving the success rate of industrial grasping, and is suitable for automated sorting and assembly scenarios such as micro-electronic components and precision mechanical parts.

This invention discloses a robotic arm grasping control method, device, electronic equipment, and storage medium. The device is applied to a robot, which includes a lifting mechanism, a motor base, an adjustment mechanism, a gripping mechanism, and a positioning camera. The top end of the lifting mechanism is fixedly connected to the bottom end of the motor base, the top end of the motor base is fixedly connected to the bottom end of the adjustment mechanism, one end of the adjustment mechanism is fixedly connected to one end of the gripping mechanism, and one side of the motor base is fixedly connected to one side of the positioning camera. This invention's device employs visual 3D positioning and hand-eye calibration, resulting in small positioning errors and adaptability to various placement postures and irregularly shaped targets. It features a dual closed-loop force control mode, balancing grasping stability and object safety, and is adaptable to various materials and objects. Its modular design allows for independent use or integration into multi-machine collaborative systems, enabling rapid actuator replacement and broad scenario coverage. Full-process anomaly monitoring and fault-tolerant protection prevent collisions, slippage, and object damage, ensuring high operational reliability.

This invention provides a robust 3D localization method for traffic scenes using a combined visual and language approach. It integrates multimodal image fusion technology with a combined visual and language inference mechanism, achieving simultaneous recovery and fusion of multimodal information under adverse weather conditions through mask-guided feature recovery and interaction models. A progressive granularity optimization strategy is employed to generate a complete target mask, and context-aware feature encoding is combined to improve semantic recognition accuracy. A high-precision 3D semantic map is constructed through multi-view fusion and geometric constraint optimization, and a large language model is used to parse natural language commands, achieving precise target localization and retrieval. This method effectively solves key problems such as low perception accuracy, fragmented semantic segmentation, and weak language interaction capabilities under adverse weather conditions, significantly improving the robustness and practicality of 3D understanding of traffic scenes. It provides more reliable technical support for fields such as autonomous driving and intelligent traffic monitoring, possessing significant engineering application value and industrial promotion potential.

The embodiment of the application relates to the technical field of large-scale landscape decoration engineering, in particular to a 3D positioning and installation method and device for a square center flower basket and electronic equipment. The method comprises the following steps: 3D scanning a small sample of the flower basket to obtain 3D original data generated by scanning; performing structured geometric data construction on the 3D original data, and performing digital modeling based on the generated structured data to generate a small sample digital model; enlarging the small sample digital model by a preset multiple to obtain an enlarged digital model; extracting the center space coordinates and the orientation angle parameters of each flower from the enlarged digital model, and positioning and installing the flowers according to the center space coordinates and the orientation angle parameters. The method can realize high-precision spatial positioning and installation of the flowers, and guarantee the structural consistency between the actual flower basket and the small sample.

This invention discloses a real-time dynamic trajectory correction system for the jacking process of a steel main beam based on three-dimensional positioning, specifically relating to the field of intelligent manufacturing technology. It includes the following modules: a multi-source sensor data acquisition module, a guide beam spatial pose calculation module, a three-dimensional trajectory deviation vector module, a jacking cylinder correction calculation module, and a dynamic correction trajectory planning module. This invention obtains the strain distribution of the guide beam by acquiring the center wavelength drift of the fiber optic grating, calculates the strain tensor by combining it with the preset cross-sectional moment of inertia, and reconstructs the instantaneous spatial shape of the guide beam caused by stress and temperature changes by integrating along the axis. By calculating the spatial deviation vector between the measured coordinates and the trajectory points in the steel main beam design blueprint, and projecting and decomposing this vector onto the action axis of each jack, and weighting it according to the resistance coefficient of the jacking bracket, the system directly generates multi-channel cylinder stroke correction amounts, realizing dynamic trajectory tracking and real-time correction based on the actual stress state of the guide beam.