468 results about "Landmark" patented technology

A system for computer assisted navigation during surgery includes computer platform that operates to identify a set of locations at which a navigated instrument is palpitating a landmark defined on a surface of a pelvic bone of a patient. Further operations determine a center of rotation for a pelvic acetabulum of the patient based on the identified set of locations at which the navigated instrument is palpitating the landmark. Operations determine an orientation of an anterior pelvic plane (APP) and / or a functional pelvic plane (FPP) of the patient based on the identified set of locations at which the navigated instrument is palpitating the landmark and based on the determined center of rotation for the pelvic acetabulum.

In estimating a position of a vehicle utilizing a global navigation satellite system (GNSS), it is desirable to exclude outliner GNSS measurements due to navigation signals via non-light-of-sight paths from the GNSS to the vehicle, but it leads to a distorted satellite geometry distribution. Complementariness between low-lying visual landmarks and healthy but high-elevation satellite measurements is explored to improve the geometry constraint. Measurements of an inertial measurement unit, low-lying visual landmarks captured by a forward-looking camera onboard the vehicle, and healthy but high-elevation satellite measurements are tightly-coupled integrated via sliding window optimization of system states used in a factor graph. To improve estimation performance, good initial guesses of system states are important. As such, initial guesses of velocity set and position set inside a sliding window are estimated simultaneously based on data of Doppler measurement, double-differenced (DD) pseudorange measurement and DD carrier-phase measurement as obtained in GNSS measurements.

Autonomous ground vehicles that are outfitted with radar sensors and inertial measurement units accurately determine states of the autonomous ground vehicles, e.g., estimates of the vehicles' positions, orientations, or velocities or accelerations along or about one or more axes, based on data captured by the radar sensors and the inertial measurement units. Where objects are detected in radar scans, the objects are determined to be static (or fixed), or dynamic (or moving), and landmarks representing static objects are identified. Constraints on estimates of states may be calculated based on doppler effects, inertial measurement unit effects, or locations of landmarks, and the states may be determined as solutions to optimization problems based on the data and the calculated constraints. Odometry messages representing the determined states may be generated and stored or utilized for any purpose.

The invention discloses a bionic joint humanoid robot navigation positioning method, device and equipment, and the method comprises the steps: monitoring a landing shock wave signal and reflection echo data of a robot, carrying out the time domain expansion of the shock wave signal to form a pulse characteristic spectrum, extracting multipath propagation components, and carrying out the time difference correlation to generate an environment response value. Screening a direct wave component by using an environmental response value, positioning a sound wave propagation trajectory, and detecting impedance mutation to construct an acoustic matrix. A strong reflection zone is identified through node analysis, a sound pressure gradient field is extracted to determine an obstacle boundary, and a landmark feature bit is determined in combination with an environmental response value. And adjusting a dynamic stride frequency interval based on a landmark feature bit, forming an adaptive detection sequence, and performing sparse processing to construct path configuration. The mechanical vibration component of the shock wave signal is used for piezoelectric conversion to generate recycled electric quantity, the recycled electric quantity is matched with path configuration power to determine a sensing period, intermittent sensing is executed to obtain detection data to construct a detection convergence field, and finally an accurate positioning window is determined and converted into position coordinates.

A computer-assisted medical system includes a user device wearable by an operator. The user device includes a display device configured to present imagery overlaid in an environment of a manipulator assembly, and a sensor configured to detect one or more landmarks in the environment. The medical system includes a controller configured to execute instructions to perform operations The operations include receiving, from the sensor, position or orientation information for the one or more landmarks in the environment, and directing a manual movement of a portion of the manipulator assembly by causing the display device to present the imagery overlaid in the environment based on the received position or orientation information.

The invention relates to the technical field of machine vision recognition, in particular to a dynamic obstacle avoidance method and system for equipment based on behavior trend prediction, equipment and a medium, and the projection range of the equipment on the ground can be effectively adjusted by introducing an image segmentation expansion scale regression mechanism driven by equipment PLC features and an image segmentation expansion prediction model. The method not only improves the fitting precision of the real boundary, but also ensures the consistency of the landmark covered by the expansion boundary, and provides stronger data support for collision modeling of image-physical fusion. A human body positioning and equipment lower edge convex edge construction method based on image bottom edge center point back projection is introduced to serve as a key link of collision modeling, so that the two-dimensional space relation between equipment and personnel is restored. Through the above method, not only is the diversity of different equipment forms paid attention to, but also the directivity and orientation characteristics of the equipment can be accurately modeled, and the stability and interpretation of collision pre-judgment are remarkably improved.

A processor-implemented method including selecting a first reference landmark from among a first plurality of candidate landmarks related to a parking area of a vehicle in a first frame image corresponding to a first timepoint, determining positioning information of the vehicle by using geometric relationship information of the selected first reference landmark with respect to the vehicle, selecting a second reference landmark from among a second plurality of candidate landmarks in a second frame image corresponding to a second timepoint, the second timepoint being temporally subsequent to the first timepoint, and updating the positioning information using the geometric relationship information of the selected second reference landmark with respect to the vehicle.

The invention provides a multi-level semantic map construction method based on multi-sensor fusion, and the method carries out the construction of an environment grid layer, and comprises the steps: constructing an environment grid map in real time through fusing perception data; scene semantic layer construction: extracting image scene semantic probability distribution by using a deep convolutional network, fusing time sequence observation through Bayesian filtering, and mapping a scene category to a grid unit by using an occupation probability model; constructing an object semantic layer, namely identifying an object by adopting a target detection network in which an information aggregation-distribution mechanism is introduced, extracting an object point cloud, and dynamically updating object semantic attributes of grid units through multi-source observation fusion; and scene atlas generation: constructing a hierarchical scene atlas which takes the marker object as a reference core and comprises a spatial topological relation. According to the method, the dynamic environment adaptability and the multi-modal data fusion precision of semantic mapping are improved, a more visual environment understanding mode is provided for the robot, and the practicability of the semantic map in robot positioning and navigation is improved.

The invention provides a positioning method and system based on multi-sensor fusion and a storage medium. Through strategies of local dynamic adaptation, global static correction and multi-source fusion, the problem of laser SLAM positioning degradation caused by warehouse environment changes is effectively solved. Local sub-graph matching adapts to a dynamic environment, and frequent reconstruction of a global map is avoided. And the dynamic weight distribution ensures the positioning accuracy in different environments. The redundant design of multiple sensors such as a laser radar / a wheel type encoder / a two-dimensional code ensures continuous operation in a complex scene. When laser matching fails, the two-dimensional code landmark provides redundant positioning. And flexible addition of new sensors is supported, and requirements of different storage scenes are met. Through combination of local subgraph matching, global observation constraint, an encoder speedometer, a two-dimensional code landmark and multi-sensor weighted fusion, the scheme not only significantly improves the positioning precision and robustness of the AGV in a dynamic environment, but also reduces the dependence on environmental consistency.

The invention provides a body navigation method, device and equipment based on brain inspiration structured spatial memory, and relates to the technical field of navigation.The method comprises the steps that a work memory module is used for determining a structured spatial memory module for searching a navigation instruction according to the complexity of the received navigation instruction, and the structured spatial memory module is used for searching the navigation instruction; the structured spatial memory module comprises at least one of a landmark memory module and a cognitive map module; responding to the navigation instruction, and performing hierarchical retrieval by using a structured spatial memory module of the navigation instruction to obtain a candidate coordinate set of the target; the candidate coordinate set comprises a plurality of candidate coordinates; determining an exploration sequence based on the confidence score of each candidate coordinate and the distance between each candidate coordinate and the coordinate of the current position of the body agent; the exploration sequence represents the exploration sequence of the candidate coordinates; and controlling the body agent to execute navigation operation based on the exploration sequence. According to the method, the robust space cognition and self-adaptive navigation capability of the intelligent agent with the body in a complex environment is realized.

A method is provided for delivering location-based information using artificial intelligence. The method includes automatically inferring a location of a user system at a geographic landmark based on location data, and triggering an AI assistant in response to the inferred location. The AI assistant generates information about the geographic landmark, and a graphical indication of the AI-generated information is displayed proximate to a graphical representation of the user on a map interface. Upon user selection of the graphical indication, a chat conversation with the AI assistant is initiated, the conversation including the AI-generated information about the geographic landmark

A computer-assisted medical system includes a user device wearable by an operator. The user device includes a display device configured to present imagery overlaid in an environment of a manipulator assembly, and a sensor configured to detect one or more landmarks in the environment. The medical system includes a controller configured to execute instructions to perform operations The operations include receiving, from the sensor, position or orientation information for the one or more landmarks in the environment, and directing a manual movement of a portion of the manipulator assembly by causing the display device to present the imagery overlaid in the environment based on the received position or orientation information.

The invention belongs to the technical field of artificial intelligence, and relates to an underwater navigation method and system based on panoramic visual perception and language model reasoning. The method comprises the following steps: acquiring a 360-degree panoramic video sequence when an underwater robot runs, pre-processing the 360-degree panoramic video sequence, dividing the 360-degree panoramic video sequence into sub-views, extracting visual features, combining position and action embedding information, and aggregating the sub-views into a space-time trajectory feature flow through a Transform network. An adaptive module is embedded in a language model, and visual track features are injected into a language hidden space by using a cross attention mechanism, so that visual-language information fusion and content reasoning are realized. Meanwhile, semantic scene representation is constructed through three-dimensional scene modeling, illusion landmarks are inhibited by means of a landmark uncertainty evaluation module, and finally reasoning output of a language model is mapped into a structured control instruction. According to the method, autonomous semantic navigation based on content understanding in an underwater environment is realized.

The invention discloses an outdoor unmanned aerial vehicle visual language navigation method based on a multi-modal memory map, and aims to solve the problems of memory discretization, insufficient space connectivity and overhigh calculation load caused by dependence on global mapping in an unstructured environment in the prior art. The method comprises the following steps: extracting semantic features from an aerial view image by using a visual language model, and constructing memory nodes in combination with a local truncated symbol distance field; establishing topological connection containing six-degree-of-freedom constraints according to a visual common-view relationship and spatial trafficability among the nodes, and performing hierarchical organization on semantic landmarks in the nodes to form a multi-modal memory map; a semantic weight is calculated based on a cross attention mechanism, and a three-dimensional semantic navigation potential field is constructed by fusing geometric accessibility probability; and determining a navigation target according to the potential field distribution, executing path search in a topology layer, and performing obstacle avoidance and track generation in a measurement layer. According to the invention, the long-voyage autonomous visual language navigation of the unmanned aerial vehicle is realized under the condition that the computing resources are limited.

The invention discloses a landing positioning method and system based on fusion of vision and inertial navigation. The method comprises the following steps: collecting vision information of a current landing area; the visual information of the front landing area is mapped to an aligned grid of a map coordinate system through homography transformation, correlation matching is conducted on the visual information and a map through a frequency domain FFT algorithm, a matching result of rough matching is obtained, and rough matching is completed; the method comprises the following steps of: mapping an area subjected to rough matching and positioning to a grid aligned with a map coordinate system by utilizing homography transformation, performing spatial domain multi-window division on a larger range of images around the area subjected to rough matching and positioning, selecting a landmark area in each sub-window by applying an interest operator, performing spatial correlation matching on the landmark area and a map by utilizing a spatial correlation method, and performing spatial correlation matching on the landmark area and the map by utilizing a spatial correlation method. Finishing fine matching; inputting all matched observation results into an extended Kalman filter, and carrying out joint estimation on the matched observation results and the observation results of the inertial measurement unit; the method has the advantages that the robustness, the precision and the engineering applicability of an existing navigation positioning method are improved.

The invention discloses an unmanned aerial vehicle navigation method and device based on vision and language, terminal equipment and a storage medium, and relates to the field of unmanned aerial vehicle navigation, and the method comprises the following steps: obtaining images of each view angle of an environment where an unmanned aerial vehicle is located and a natural language navigation instruction; the method comprises the following steps: extracting visual features of an image of each view angle through a pre-trained visual language model, and extracting landmark word features corresponding to each landmark phrase in a natural language navigation instruction through a pre-trained large language model; obtaining a potential landmark candidate item corresponding to each landmark phrase through a cross attention mechanism; determining all target landmarks through a pre-trained large language model; obtaining a feasible path according to all target landmarks; and realizing navigation of the unmanned aerial vehicle based on the feasible path. By implementing the invention, the problem that the navigation of the unmanned aerial vehicle in a small crowd scene or an unknown environment is difficult to realize by the existing unmanned aerial vehicle navigation technology can be solved.

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and / or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.

The invention provides a dynamic planning method and system for a low-altitude route of an unmanned aerial vehicle, and relates to the technical field of intelligent planning, and the method comprises the steps: receiving a new delivery point instruction, obtaining a three-dimensional coordinate, carrying out the task integration of a new delivery point and an existing delivery point, defining a geographic range through three objects, namely a communication base station, a wireless tower and a permanent landmark building, and carrying out the task integration. Performing space structure division on the geographic range, and disassembling the geographic range into uniformly distributed three-dimensional voxel grids; and generating an adjustment value according to the coordinate interval of the three-dimensional voxel grid, including the delivery point density, the linear distance with the landmark building and other structural attributes, and adjusting the task integration result by using the adjustment value to obtain the adjusted task integration result of all delivery points. According to the invention, safe, efficient and accurate route planning of a multi-delivery-point dynamic newly-added scene in a complex low-altitude environment is realized.

The invention belongs to the technical field of computer vision and intelligent sensing, particularly relates to a positioning system for monitoring blind area multi-sensor space-time calibration, and aims to solve the problems of monitoring blind areas and track breakage caused by non-uniform space-time reference of multi-source heterogeneous sensors. The system comprises a space-time reference generation unit, distributed sensing nodes, a joint calibration engine, a real-time positioning calculation module and a self-adaptive feedback controller. Through nanosecond time synchronization, space registration based on a static landmark and a mobile calibration carrier and a dynamic re-calibration mechanism of closed loop feedback, continuous smooth trajectory output of cross-device target tracking is realized. The system supports layered architecture and block chain evidence storage, improves expandability and security, and is suitable for smart cities and industrial automation scenes.

The invention provides an urban visual navigation method, device and equipment based on a large language model and a storage medium, and relates to the technical field of visual navigation, the method comprises the following steps: constructing a fine tuning data set based on a plurality of first streetscape images of a target city, and performing fine tuning on a multi-modal large language model by using the fine tuning data set to obtain a multi-modal large language model; the annotation information of each first streetscape image comprises landmark position and distance information corresponding to each first streetscape image; determining an intelligent agent system for urban visual navigation based on the multi-modal large language model after fine tuning; based on the natural language description of the target position, the intelligent agent system repeatedly executes the processes of sensing, reflecting, planning and acting until the target navigation task is completed, the target position description comprises the position relation between the target and the landmark, and the position relation comprises the relative orientation and distance; the target navigation task is used for representing a navigation task from the current position of the intelligent agent system to the target position. According to the invention, autonomous navigation in a city scene is realized through the intelligent agent system.

The invention discloses a robust visual inertia SLAM method fusing point-line flow features, a storage medium and equipment, and the method comprises the steps: 1, carrying out the image enhancement of an input image based on an SSR-CLAHE algorithm, and carrying out the data preprocessing of an SLAM system for the original data of an IMU and the enhanced image, and obtaining the IMU pre-integration data and visual feature data; 2, performing time alignment processing on the visual feature data and the IMU data to enable the visual feature data and the IMU data to be in the same time frame, initializing a camera pose, a speed and IMU offset, performing triangularization on visual features to generate a preliminary 3D new road sign, and constructing an initial environment map; 3, inputting the 3D new landmark data including the initial camera pose, speed and IMU offset and in the constructed initial environment map into a sliding window for optimization, and estimating the pose and speed of the camera and the offset of the IMU in real time; and step 4, carrying out closed-loop detection and optimization. And the positioning precision and robustness are improved so as to adapt to a complex and changeable indoor environment.

The invention discloses an unmanned aerial vehicle natural language multi-modal navigation method and system based on a multi-dimensional thinking chain. The method mainly comprises the following steps: constructing a software-in-the-loop simulation environment deeply integrated with task planning software, and collecting a live-action image with a time-space stamp; secondly, the natural language task instruction is analyzed, and key landmarks and position coordinates of the key landmarks are recognized in combination with a real scene image; then the recognition result and the structured task sequence are input into a multi-dimensional thinking chain reasoning engine in parallel to be processed; and finally, integrating the output of the multi-dimensional thinking chain inference engine, generating a flight action sequence, and issuing the flight action sequence to a flight control system to complete a navigation task. According to the method, the explicit thinking chain reasoning process is embedded into each step of navigation decision and is deeply integrated with the existing task planning ecology, so that the autonomy, adaptability and task success rate of the unmanned aerial vehicle in an unknown or dynamic environment can be remarkably improved.

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

The invention discloses a vision and inertial navigation integrated navigation method, system and equipment in a weak vision environment and a medium, and belongs to the technical field of navigation, by fusing an inertial measurement unit and landmark focal plane projection data, the navigation precision of the system under the adverse conditions of low texture, dynamic shielding and the like is remarkably improved, and the navigation continuity can be maintained; according to the method, an indirect Kalman filter is used for carrying out feedback correction on initial navigation parameters, noise interference and inertial navigation accumulative errors are effectively inhibited, navigation errors are remarkably reduced for state estimation, and the robustness and adaptability of the system are enhanced; the method does not depend on complete visual resolving, landmark projection coordinates are directly used, dependence on visual information integrity is reduced, and the system can stably operate even if only a small number of landmarks or a single landmark is visible.

The invention provides a visual navigation optimization method and device, a computer system and a storage medium, and relates to the technical field of visual synchronous positioning and mapping. The method comprises the following steps: acquiring relative pose information and a semantic tag of a target semantic object, and combining the relative pose information and the semantic tag into semantic object information corresponding to the target semantic object; converting the semantic object information into landmark information; fusing the landmark information as a constraint node into global optimization through a graph optimization framework; and generating a navigation path or a space control parameter and performing navigation. According to the optimization method of the visual navigation, the effect of optimizing the synchronous positioning and mapping back-end module is remarkably improved, the semantic information of the map is enriched, the understanding and usability of the map are enhanced, and the robot can realize more intelligent and efficient navigation in a complex environment. Meanwhile, optimization precision and stability are improved, information redundancy and inconsistency are reduced, resource use efficiency is optimized, and wide application prospects are shown.

Example aspects of the present disclosure are directed to computing systems and methods for hand tracking using a machine-learned system for palm detection and key-point localization of hand landmarks. In particular, example aspects of the present disclosure are directed to a multi-model hand tracking system that performs both palm detection and hand landmark detection. Given a sequence of image frames, for example, the hand tracking system can detect one or more palms depicted in each image frame. For each palm detected within an image frame, the machine-learned system can determine a plurality of hand landmark positions of a hand associated with the palm. The system can perform key-point localization to determine precise three-dimensional coordinates for the hand landmark positions. In this manner, the machine-learned system can accurately track a hand depicted in the sequence of images using the precise three-dimensional coordinates for the hand landmark positions.

The invention discloses a waypoint prediction method fusing instruction landmark features in visual language navigation, and belongs to the crossing field of artificial intelligence and computer vision. The method is realized through the following steps: firstly, extracting landmarks and co-occurrence landmarks thereof from a natural language instruction by utilizing a large language model, and generating a candidate landmark sequence; then, the occurrence probability of the candidate landmarks in actual observation is calculated based on a visual large model CLIP, and noise interference is dynamically corrected and suppressed through a learnable co-occurrence scoring module; secondly, fusing the corrected landmark features with the depth visual features, inputting a double-layer Transform model to model a spatial relationship, generating a waypoint probability heat map, and outputting adjacent waypoints through non-maximum suppression (NMS); and finally, topological mapping is carried out on the predicted waypoints, an optimal path is obtained through cross-modal path planning, and an intelligent agent is guided to execute low-level actions. The method does not need to depend on a predefined environment map or manually annotate data, navigation errors in a continuous environment are remarkably reduced, the generalization ability of unseen scenes is improved, and the method is suitable for open world navigation scenes such as robot navigation and automatic driving.

The invention discloses a map-free electric power inspection robot navigation system and method based on large model driving and a related device. The invention provides a map-free electric power inspection robot navigation system and method based on large language model driving and a related device, and the system comprises a voice text conversion module which is used for carrying out text conversion on voice data obtained by an inspection robot to obtain voice text information; the large voice model processing module is used for extracting landmarks, electric power facility components and key adverb information from the voice text information and generating an inspection command; the semantic segmentation module is used for generating a text mask from the acquired environment image information, and the text mask comprises the electric power facility landmark and the electric power facility component in the environment image; the navigation planning module is used for generating an optimal navigation path according to the inspection command and the electric power facility landmarks and the electric power facility parts in the environment image; according to the invention, the problems of high map updating cost and navigation deviation caused by environment change in the prior art are solved, so that the navigation precision, coping capacity and operation efficiency of the electric power inspection robot are improved.

The invention discloses a surgical navigation image real-time registration method and system based on multi-modal body surface mark tracking. The registration method comprises the following steps: S1, constructing an A-type three-dimensional model of the head of a patient before an operation; s2, constructing a B-type three-dimensional model of the head of the patient in real time during an operation; s3, in the operation, at the current time point, registration of a depth mode, registration of a texture mode and registration of a multi-view tracking mode are carried out at the same time; and S4, repeating the step S2 to the step S3 at the next time point, and simultaneously realizing the registration of the depth mode, the registration of the texture mode and the registration of the multi-view tracking mode, thereby realizing the real-time registration of the surgical navigation image. According to the registration method, real-time registration of the surgical navigation image and the body position of the patient can be achieved, the registration operation is simple and convenient, the registration time is shorter than 30 seconds, and compared with an existing registration method, the time needed for registration is greatly shortened.