370 results about "Landmark" patented technology

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 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

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 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 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.

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.

Optical sensors (e.g., cameras) and (e.g., IR) illumination sources may be distributed in an environment (e.g., an interior space such as a cabin or cockpit of an ego-machine) and synchronized to generate frames of sensor data, which may be used to reconstruct 3D geometry and / or 3D pose of an occupant, operator, or other object in the environment. For example, stereo vision may be used to generate one or more depth maps from image data generated using different cameras, the depth map(s) may be transformed into a 3D point cloud, and surface reconstruction may be applied to reconstruct the 3D geometry of surface(s) in the environment. A 3D pose, one or more keypoints (e.g., facial landmarks), or some other representation of the shape of the reconstructed surface(s) may be extracted from the reconstructed surface and used in one or more downstream tasks, such as driver and / or occupant monitoring tasks.

The invention belongs to the technical field of Internet of Things and geographic information, and particularly relates to an intelligent water meter reading platform and method based on high-precision Internet map landmark positioning. The data acquisition module is used for acquiring water meter reading and equipment running state data and acquiring original position data according to a preset positioning strategy; the data transmission network is used for providing a bidirectional wireless data communication link between the intelligent water meter terminal and the intelligent water meter reading platform; the invention discloses an intelligent water meter reading platform. The position information of the intelligent water meter can be accurately and reliably associated with a specific geographic landmark by transferring the positioning precision challenge to the powerful calculation and data processing capability of a platform side and combining a feedback mechanism of manual calibration; therefore, the core contradiction that reliable, accurate and automatic association between the water meter and the high-precision internet map landmarks cannot be achieved in the prior art is fundamentally solved, and a solid technical support is provided for refined operation of intelligent water affairs.

In another embodiment, a hip joint navigation jig is provided that includes an anatomical interface comprising a bone engagement portion. A registration jig is also provided that is coupled, e.g., removeably, with the anatomical interface. A rotatable member is provided for rotation about an axis that is not vertical when the jig is mounted to the bone adjacent to a hip joint and the registration jig is coupled with the anatomical interface. An anatomy engaging probe is coupled with the rotatable member for rotation about the axis and is translatable to enable the probe to be brought into contact with a plurality of anatomical landmarks during a procedure. An inertial sensor is coupled with the probe to indicate orientation related to the landmarks, the sensor being disposed in a different orientation relative to horizontal when the probe is in contact with the landmarks.

Systems and methods for generating map data are disclosed herein. One embodiment of a map-data generation system receives, from one or more vehicles that traveled within a region, a set of estimated locations for each landmark in a plurality of landmarks within the region. The system also generates a base zone map of the region that represents roadways as edges and intersections as junctions. The system also transforms, to edge-relative coordinates, the spatial coordinates of the sets of estimated locations. The edge-relative coordinates improve a Global Nearest Neighbor (GNN) algorithm in performing data association to generate a final estimated location for each landmark. The system also outputs a final zone map that includes the final estimated location for at least one landmark. The final zone map is used for one or more of localization, navigation, and path planning to control an autonomous vehicle.

Facilitating navigation localization for a mobile robot operating autonomously can include a deployment of a retractable landmark in a work area in response to an operational threshold not being met for a mobile robot to autonomously operate traversing the work area. The operational threshold not being met can be based on a determination of when landmarks in the work area for the mobile robot to use as perimeter markers do not meet the operational threshold.

A geometric structure aided visual localization method and a system implementing the method are provided. The method includes retrieving a 3D map of initialization location data, the 3D map being constrained by visual structures and geometric structures and modeled by a Gaussian mixture model of a set of Gaussian distributions with mapped landmarks; acquiring a series of real-time image frames by a camera of a mobile system; and for each real-time image frame: extracting local features from the real-time image frame; predicting a camera pose corresponding to the real-time image frame; creating a key frame by tracking the predicted camera pose in the 3D map; identifying temporally visible landmarks with respect to the created key frame; acquiring 2D-3D correspondences between the local features and the temporally visible landmarks; and localizing the mobile system by estimating a state of the real-time image frame based on the 2D-3D correspondences.

To provide a more efficient system / method for eye motion tracking.SOLUTION: A system and method for eye motion tracking. An anchor point and a plurality of auxiliary points are selected from the reference image. Individual live images in the sequence of images are then searched for a match between the anchor point and the ancillary point. First, an anchor point is found, and then the search for individual auxiliary points is limited to a search window defined by the known distance and / or orientation of the auxiliary point to be searched relative to the anchor point.SELECTED DRAWING: Figure 4

Methods and system are herein provided for determining a time shift between images captured by means of different cameras. In one example, a time shift determination unit comprises a computing device comprising one or more processors and a memory, wherein the computing device is configured to determine a defined plurality of landmarks in each image of a first plurality of successive images of a defined environment captured by means of a first camera, each landmark being defined by an x-coordinate and a y-coordinate in each of the respective images, determine the defined plurality of landmarks in each image of a second plurality of successive images of the defined environment captured by means of a second camera, each landmark being defined by an x-coordinate and a y-coordinate in each of the respective images, for each image captured by means of the first camera and the second camera, determine a mean x-coordinate or a mean y-coordinate for the defined plurality of landmarks in the image, compile a first chart representing a progression of the mean x-coordinate of the defined plurality of landmarks over the first plurality of successive images, and a second chart representing a progression of the mean x-coordinate of the defined plurality of landmarks over the second plurality of successive images, or compile a first chart representing a progression of the mean y-coordinate of the defined plurality of landmarks over the first plurality of successive images, and a second chart representing a progression of the mean y-coordinate of the defined plurality of landmarks over the second plurality of successive images, determine a similarity measure of the first chart and the second chart, perform a defined number of shifts of the second chart with respect to the first chart, wherein with each shift the second chart is shifted by a defined number of images, and for each shift of the plurality of shifts determine a similarity measure of the first chart and the shifted second chart, and evaluate the determined similarity measures in order to determine a time shift between the first plurality of successive images and the second plurality of successive images.

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to a Registration Engine that identifies a region of interest from an infrared image portraying a target portion of a physical anatomy in a current physical pose. The Registration Engine generates a 3D point cloud of the region of interest. The Registration Engine identifies region of interest isosurface data by detecting matches between isosurface data and the 3D point cloud. The Registration Engine determines display location, at the target portion of the physical anatomy, for the region of interest isosurface data. The Registration Engine renders an Augmented Reality (AR) display of medical data with respect to the display location.

Techniques are disclosed to determine driver scoring that consider deviations in map and sensor data in driver score computations. The deviations can be based on deviations from an optimum driving path, and include the determination of vectors between a reference point and one or more landmarks, and one or more reference vectors between the reference point the landmark(s) when traveling on an optimum driving path. A difference between the vectors may then be used determine the deviations from the optimum driving path. In contrast to the conventional approaches, the use of positional deviations (e.g. determined from road markings) in the computation of driver scores allows for improved driver scoring techniques and driver characterizations.

A method for tracking a hand of a user immersed in an Extended Reality (XR) session includes determining a context of an operation of a Head-Mounted Display (HMD) device and a position of the hand with reference to an input scene; estimating landmarks associated with the hand based on the context; classifying the landmarks into one of a first group of one or more occluded landmarks and a second group of one or more non-occluded landmarks; predicting a position of the first group using an artificial intelligence (AI) model based on obtaining hand kinematics associated with the user and the context of the operation; rendering the hand in the XR session based on the second group and the predicted position of the first group; and tracking the hand based on rendering the hand in the XR session.

Training framework for creating an artificial intelligence (AI) system for estimating a position of an anatomical landmark by text inputs. The training framework includes providing a context-set comprising a list of names of anatomical landmarks, and a position-list comprising position-tokens being expressions referring to relative positions. A plurality of question-prompts asking for the relative position of a landmark are generated by using varying combinations of the landmarks and position-tokens of the context-set and the position-list. A number of target-landmarks to each question-prompt are generated by inputting the question-prompts in a large language model. The answer is parsed for landmarks and the found landmarks are defined as target-landmarks. A plurality of training-datasets are formed, wherein each training-dataset comprises the landmark, the position-token from a question-prompt and the target-landmark from the answer to this question-prompt. The AI-system is trained with the training-dataset and additional spatial coordinates of a part of the landmarks of the context-set.