6245results about "Navigation instruments" patented technology

Approaches presented herein provide for the use of language models to generate tokenized descriptions of physical environments. In at least one embodiment, sensor and / or observational data can be obtained for an environment and used to generate a set of perception data. The perception data can be analyzed, along with approximate positional data within the environment, to identify a set of aligned map data. The aligned map data and perception data can be provided as input to a trained language model, which can be trained to correlate and / or fuse the information to generate a single, consistent representation of the environment. The language model can output a tokenized description of the environment, which can be in a domain-specific language, that is a compact but robust textual description of the environment.

The invention discloses a multi-target scene visual SLAM (Simultaneous Localization and Mapping) method fusing target semantics and Gaussian splashing. The method comprises the steps that input data are preprocessed, two-dimensional space masks and semantic information corresponding to the two-dimensional space masks are extracted, global semantic identifiers are distributed to the two-dimensional space masks corresponding to a background and each target, and a target semantic segmentation map is obtained; reading first frame RGBD data as a current frame, acquiring a target semantic segmentation map corresponding to the current frame, and initializing camera pose parameters; respectively establishing an initial target Gaussian splashing model for the background and each target; adopting a Gaussian splash algorithm to render all the target Gaussian splash models to a next frame, and generating an RGB rendering image, a depth rendering image and a target semantic rendering image of the next frame; and designing a loss function between the next frame of rendered image and the corresponding real image, optimizing the pose of the camera, and updating the parameters of the target Gaussian splash model. According to the invention, simultaneous positioning and map construction of multiple target scenes are realized.

A display system of an aircraft includes a display device defining a display area, a vision system for generating an image of an operating environment of the aircraft on the display area, data processors operatively coupled to the display device and the vision system, and non-transitory machine-readable memory operatively coupled to the one or more data processors and storing instructions executable by the one or more processors and configured to cause the one or more processors to, when a condition of the aircraft is met, cause an alteration of a display of flight information on the display area to declutter a cutout region in the image generated by the vision system, the cutout region devoid of imaging generated by the vision system and displaying an Approach Lighting System (ALS) of a runway of intended landing for the aircraft.

Systems and methods are provided for promoting stable aircraft approach conditions. The system comprises a display device that is onboard an aircraft and a controller in communication with the display device. The controller is configured to, by a processor: receive data that includes information relating to an action configured to stabilize an approach of the aircraft during landing thereof and a recommended timing of performing the action relative to a predetermined flight plan of the aircraft, and render a first visual element on the display device that is configured to display the action relative to the flight plan and dynamically indicate the recommended timing of performing the action relative to a geographic position of the aircraft along the flight plan.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention discloses a navigation matching correction method based on an inspection robot, and the method comprises the steps: building a feature database through the deployment of a physical calibration object and the recognition of a natural feature object, providing a reliable positioning reference for a robot, achieving the coarse positioning through the fusion of visual and laser radar data in a positioning process, and introducing a dynamic credibility evaluation mechanism. The positioning reliability is quantified in real time through an exponential decay model, when the credibility is lower than a threshold value, the system automatically triggers a compensation behavior to re-search features, in the aspect of multi-robot cooperation, secondary positioning correction is achieved through track matching and data fusion, high-confidence-coefficient reference data are screened through a clustering algorithm, the group positioning precision is improved, and the positioning accuracy is improved. Aiming at a key inspection area, multi-angle image matching is adopted to realize fine positioning, a positioning error is dynamically corrected through a sliding window, the continuity and accuracy of robot navigation in a complex environment are remarkably improved through closed-loop correction and self-adaptive optimization, and the method is suitable for intelligent inspection requirements of railway trains.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention provides an unmanned aerial vehicle surveying and mapping method and system, electronic equipment and a storage medium, and relates to the technical field of unmanned aerial vehicle surveying and mapping. The method comprises the following steps: carrying out base station position planning based on geographic data and preset factors, and arranging a CORS platform base station; then, acquiring a remote sensing image to construct a three-dimensional model, and performing semantic segmentation to obtain key ground feature information; and generating a multi-modal environment semantic map for surveying and mapping route planning based on registration of the key surface feature information and the geographic data. The unmanned aerial vehicle obtains surveying and mapping data, CORS positioning information and surveying and mapping image data in real time according to the surveying and mapping route, and environment semantic positioning data is obtained based on the surveying and mapping image data and multi-mode environment semantic map registration. And finally, performing registration processing by comprehensively utilizing the CORS positioning information, the environmental semantic positioning data and the surveying and mapping data to obtain target surveying and mapping data. Through the scheme, the surveying and mapping accuracy of the unmanned aerial vehicle is improved.

The invention relates to the technical field of image processing, and discloses a steel bar positioning control method, system and device based on intelligent scanning and a medium. The method comprises the following steps: establishing a global reference coordinate system through target ball space measurement; scanning the mold table according to the reference coordinate system and extracting boundary features; dividing an operation area and planning a path based on mold table positioning data; performing hand-eye calibration to obtain a pose matrix; filtering the point cloud data and extracting steel bar features; and analyzing the steel bar intersection points and generating an optimized binding instruction. According to the method, the reinforcing steel bar intersection points with different intervals are recognized in a self-adaptive mode, the optimal binding path is generated through the global optimization algorithm, and meanwhile the smoothness and continuity of movement of the mechanical arm are guaranteed.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention relates to the technical field of synchronous positioning and map construction, in particular to a geometric-semantic collaborative fusion mobile robot three-dimensional semantic map construction method, which is used for performing deep fusion on a geometric map and multi-view semantic information so as to solve the problem of difference of the two types of information in resolution, scale and dimension. The geometric map mainly provides structured information of the environment, and the semantic information describes object categories and features thereof in the environment. Direct fusion of the two types of information often causes spatial positioning errors and semantic information loss or redundancy problems. According to the method, a geometric map containing environmental structured information is constructed through an SLAM technology, and meanwhile, object classification, attribute labeling and semantic relationship analysis are performed on multi-view image data by utilizing the semantic information extraction capability of a large model. And finally, precise fusion of the geometric map and multi-view semantic information is realized, and meanwhile, the object recognition and semantic understanding capabilities of the robot in a complex scene are improved.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

In various examples, a technique for generating a path between a current location and a target waypoint is disclosed that includes receiving a route plan that is associated with a plurality of waypoints representing locations in a physical environment. The technique also includes identifying a search space that includes the route plan, and identifying a target waypoint of the plurality of waypoints—the target waypoint being in a portion of the search space between a current location of a mobile robot and an end waypoint of the route plan. A path between the current location of the mobile robot and the target waypoint may then be generated.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention discloses a field unstructured road real-time segmentation system and method for agricultural machinery autonomous navigation, particularly relates to the field of agricultural machinery autonomous navigation, and is used for solving the problems of high precision and low delay of real-time segmentation of a driving area and an obstacle in a field unstructured road environment. The image stability under complex illumination is improved through brightness balance, and texture and contour features are extracted through lightweight multi-scale coding; texture deflection and contour focusing are normalized into probability distribution, direction consistency is analyzed based on modal competition fusion, direction embedding is dynamically adjusted, and deviation is avoided; guiding branch activation of a BiSeNet decoder by the direction perception tensor, and outputting a driving area probability graph and an edge heat graph; and continuous frame consistency filtering is used for eliminating instantaneous fluctuation, a stable segmentation mask and a boundary position vector are generated, and the path stability and operation continuity of navigation control are ensured.

The invention discloses a scene-level trajectory prediction method, system, medium and equipment based on a conditional diffusion model in the technical field of intelligent driving, and the method comprises the steps: obtaining a multi-agent trajectory feature collected at a current moment and a navigation constraint condition of agent motion extracted based on a vehicle position and urban high-precision map data, and inputting the trained conditional diffusion model, and outputting a future multi-modal trajectory prediction result. According to the method, multi-source heterogeneous data is deeply fused, so that the generalization ability of the model in a complex traffic scene is improved, particularly, high prediction reliability can be kept in a rare scene which is not covered by training data, and the bottleneck that a traditional method is difficult to cope with diversified scenes is solved; according to the method, the conditional diffusion model is adopted, the multi-modal trajectory distribution is learned through the iterative denoising process, the semantic probability and the direction matching probability are combined, the prediction trajectory is effectively restrained to conform to the road topology and traffic rules, and the error accumulation effect of long-term prediction is reduced.

In various examples, segmented map creation for autonomous and semi-autonomous systems and applications is described herein. For instance, systems and methods described herein may segment a map into various portions, where the systems and methods then update the map based at least on the segmentation. For instance, based at least on receiving instances of data from machines navigating with an environment associated with the HD map, the instances of data may be associated with various portions of the map. Additionally, metrics associated with the portions of the map may be updated, such as by indicating the numbers of instances of data that are associated with the portions of the map. As such, when a metric associated with a portion of a map is satisfied, such as by reaching a threshold, the portion of the map may be updated without updating one or more other portions of the map.

The invention relates to the field of environmental noise monitoring, and discloses a noise monitoring point position automatic determination method fused with a noise map. The method comprises the following steps: acquiring a base map and sound source data of a target area, layering the base map according to a preset interval in a vertical direction, and carrying out gridding processing to obtain a three-dimensional grid; performing spatial analysis on the sound source data to obtain a sound source intensity matrix, and mapping the sound source intensity matrix to a three-dimensional grid to obtain a three-dimensional noise map; the base map comprises building structure data; performing three-dimensional path exploration on the three-dimensional noise map based on an ant colony algorithm to obtain a noise hotspot path and a pheromone concentration distribution diagram; and carrying out site selection analysis based on a genetic algorithm on the noise hotspot path and the pheromone concentration distribution diagram to obtain a Pareto optimal monitoring site set, the Pareto optimal monitoring site set being used for indicating site selection of the noise automatic monitoring site. By adopting the method, accurate and efficient automatic noise monitoring site selection in a complex urban environment can be realized, and the urban noise monitoring and treatment level is improved.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention provides a map construction method and system based on laser vision dynamic weighted fusion, and belongs to the technical field of data processing, and the method comprises the steps: obtaining an RGB image and a depth image through a vision sensor, and obtaining point cloud data through a laser radar; aligning the depth image with the RGB image, and removing invalid depth pixels; based on an ORB-SLAM2 algorithm, performing visual SLAM, extracting features of the depth image and the RGB image, and outputting sparse visual point cloud data; based on a Gmapping algorithm, performing laser radar SLAM, and outputting a 2D occupied grid map; projecting the sparse visual point cloud data into a 2D occupation grid map, and calculating the semantic occupation probability of each grid; calculating the geometric occupancy probability of each grid through an anti-sensor model; performing weighted fusion on the semantic occupancy probability and the geometric occupancy probability, and calculating a fusion occupancy probability; and generating a fusion map according to the fusion occupation probability.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention provides a photovoltaic cleaning robot sensor data fusion and obstacle avoidance method and system, and relates to the technical field of photovoltaic cleaning robots, and the method comprises the steps: obtaining multi-source sensor data, carrying out the probability distribution modeling through employing a Gaussian mixture model, and achieving the data fusion through employing a Kalman filtering algorithm of an adaptive covariance matrix, and constructing a three-dimensional semantic map, marking obstacle information, and dynamically adjusting a motion track in combination with global path planning and a fuzzy logic controller of a local obstacle avoidance layer to realize smooth obstacle avoidance of the photovoltaic cleaning robot. The environment perception capability and the obstacle avoidance efficiency of the photovoltaic cleaning robot are improved, the collision risk is reduced, and the safety and the efficiency of photovoltaic panel cleaning operation are guaranteed.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The described aspects and implementations enable training and deploying of accurate one-shot models capable of predicting trajectories of vehicles and other objects in driving environments. The disclosed techniques include, in one implementation, obtaining training data that includes a training input representative of a driving environment of a vehicle and one or more ground truth trajectories associated with a forecasted motion of the vehicle within the driving environment. The one or more ground truth trajectories are generated by a teacher model using the training input. The techniques further include training, using the training data, a student model to predict one or more trajectories of the vehicle and / or objects in the driving environment of the vehicle.

The invention provides an unmanned aerial vehicle multi-mode navigation positioning method, device and equipment, and the method comprises the steps: obtaining GPS, inertia and visual navigation mode positioning data through determining a target positioning precision index, and carrying out the evaluation to generate a multi-mode navigation state set; performing performance competition on each navigation mode based on the state set, establishing an inter-mode negotiation mechanism, and determining a mode priority sequence; establishing a navigation mode credibility matrix in combination with the priority sequence, and establishing an adaptive weight distribution sequence; analyzing a weight change trend and mode switching resource overhead, carrying out navigation mode dynamic screening through switching cost quantitative evaluation, and determining a dominant navigation mode and an auxiliary navigation mode; and performing frequency characteristic analysis on the dominant and auxiliary modes, determining a frequency domain fusion strategy based on a frequency coherence coefficient, generating a fusion positioning track, and outputting a high-precision positioning result through precision evaluation to realize intelligent multi-mode navigation positioning.

The invention relates to the technical field of path planning, and discloses a real-time path data planning processing method and system based on SLAM. The method comprises the following steps: performing semantic-geometric joint feature extraction through a laser radar and an RGB camera to obtain joint observation data; performing covariance propagation on the SLAM state vector based on semantic constraint Kalman filtering to obtain a positioning covariance and a covariance ellipse; performing constraint propagation on the semantic object set to generate virtual feature points to construct an enhanced feature map; environment grid safety scoring is carried out according to the semantic risk weight and the obstacle distance; semantic cost and SLAM uncertainty processing is carried out through bidirectional A * search, and a real-time navigation path is generated. According to the method, the technical problem of insufficient comprehensive consideration of semantic security constraint and positioning uncertainty in real-time path planning based on SLAM in a feature sparse environment is solved.