836 results about "Landmark" patented technology

Three-dimensional outdoor positioning can be considerably enhanced by using various 3D imaging techniques, LiDAR ranging measurements of surrounding landmarks, and Inertial Measurement Units (IMUs) reaching accuracies in the centimetres range. These technologies, in conjunction with global navigation satellite systems (GNSS) such as GPS, GLONASS, Galileo, and BeiDou, provide autonomous geo positioning. It is crucial to acknowledge the constraints of dead reckoning, which may result in mistakes in positional precision. Integrating modern imaging and correction techniques can significantly improve the accuracy and dependability of outdoor localisation. The amalgamation of various 3D imaging methodologies with LiDAR enhancements improves the comprehension and ranging of the environment, tackling the difficulties presented by GNSS constraints. The integration of IMUs with satellite systems ensures the provision of continuous traveling positioning data, even in instances of obscured or faulty satellite signals. This comprehensive approach improves outdoor location precision and expands the use of these technologies across other domains, such as autonomous navigation, robotics, and geospatial analysis, resulting in more effective and dependable positioning solutions. The hardware system consists of a swarm of sensors, including GNSS, INS, IMU, LiDAR, stereo imaging, and RGB technologies, which are combined to generate three-dimensional colored representations of natural landscapes and urban environments. This system reconstructs topographical features, enabling detailed mapping and analysis. It produces comprehensive three- dimensional sensory data, including geo-location and POI identification, and incorporates advanced image processing techniques, such as edge detection, for precise spatial awareness and environmental understanding. The system is designed with a compact form factor, making it easily integrated into wearable devices and autonomous vehicles. The integration of these technologies forms a sophisticated network of swarm intelligence, crucial for autonomous vehicle positioning and navigation. Particle swarm optimization (PSO) is used to address non-deterministic navigation, with different coupling fusion methods available. The Neuro-fuzzy PSO AI Positioning and Navigation System generates three-dimensional sensory data, providing detailed information about geographical distribution and dead reckoning areas.

This document describes methods and systems for vehicle localization based on radar detections. Radar localization starts with building a radar reference map. The radar reference map may be generated and updated using different techniques as described herein. Once a radar reference map is available, real-time localization may be achieved with inexpensive radar sensors and navigation systems. Using the techniques described in this document, the data from the radar sensors and the navigation systems may be processed to identify stationary localization objects, or landmarks, in the vicinity of the vehicle. Comparing the landmark data originating from the onboard sensors and systems of the vehicle with landmark data detailed in the radar reference map may generate an accurate pose of the vehicle in its environment. By using inexpensive radar systems and lower quality navigation systems, a highly accurate vehicle pose may be obtained in a cost-effective manner.

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.

System for automatically generating AR landmarks for guiding a novice user while performing maintenance operations in a device, such as a mechanical or electronic device. The system comprises one or more video cameras are used to acquire video segments of a sequence of maintenance phases, a computer (with a least one processor) that runs an operation software, for training a Machine Learning (ML) model to identify and classify predetermined parts of the device, tools for performing the maintenance operations by manipulating the state of the parts, manual operations (such as hand gestures) of a professional worker, while using the tools, during manipulation according to a workflow being a sequence of phases for carrying out the maintenance operations. Each phase is a predetermined plurality of corresponding operations performed by the professional worker in any order, to be completed before moving to the next phase. The video segments are processed by the processor and operating software to automatically generate an interaction file using the trained model. The interaction file is adapted to encode the workflow in the form of a collection of landmarks, manual operations, and the relation between them, using a playable format; associate each landmark with a corresponding phase; determine starting and ending landmarks for each phase and determine transitions between completed phases and their corresponding consecutive phases. A player is used to play the interaction file in AR user interface that is worn by the novice user. The player generates graphical guiding visual signs and animations representing each of the phases and transitions, optionally generates audio guiding instructions to be played with corresponding visual signs and animations and adds the graphical guiding visual signs and animations (along with the optional audio guiding instructions) to the AR user interface.

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.

A computer-implemented method comprises obtaining a source image and a target image, determining a first point cluster by processing the source image using a machine learning mode, determining a second point cluster by processing the target image using the machine learning model, uplifting the first point cluster to obtain an uplifted first point cluster, uplifting the second point cluster to obtain an uplifted second point cluster, estimating a transformation that maps the uplifted first point cluster to the uplifted second point cluster, applying the estimated transformation to the uplifted first point cluster to obtain a candidate reconstruction of the uplifted second point cluster, and updating the machine learning model to reduce a loss function including a term penalizing a difference between the uplifted second point cluster and the candidate reconstruction of the uplifted second point cluster.

The present disclosure relates to a method for detecting a landmark of the head and neck based on artificial intelligence. The method includes an operation of obtaining an original computerized tomography (CT) image of a head and a neck of a patient, an operation of converting the obtained original CT image into a low-resolution CT image, an operation of detecting a first landmark by inputting the low-resolution CT image into a landmark detection model, an operation of detecting a peripheral area based on the detected first landmark of the head and neck, an operation of restoring an original CT image of the detected peripheral area, and an operation of detecting a second landmark by inputting the restored image of the peripheral area into the landmark detection model.

The invention discloses a map information updating method and system, and relates to the technical field of map updating, and the method comprises the steps: according to each road sign object, using an editing distance and a cosine similarity to measure a first similarity between symbol elements in an element set of the road sign object and symbol elements in a map element set; measuring a second similarity between the color elements in the element set of the road sign object and the color elements in the map element set by using the Euclidean distance; measuring a third similarity between a central point position element in the element set of the road sign object and a central point position element in a map element set by using a Hausdorff distance; taking the intersection-union ratio of the contour frame elements in the element set of the road sign object and the contour frame elements in the map element set as fourth similarity; determining whether the four similarities are matched or not through the four similarities; if yes, updating the map. The map can be updated in time, and the real-time performance is good.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for image processing that involves annotating landmarks on real two-dimensional images. In one aspect, the methods include generating a training set of images of an object for landmark detection. This includes receiving a collection of real images of an object, estimating a pose of the object in each real image in a proper subset of the collection of real images, creating a collection of surrogate images of the object for the training set using the estimated poses and a three-dimensional model of the object.

According to the present invention there is provide a method a method for matching features in an image with landmark representations in a landmark map, the method comprising: providing at least one image; extracting from the image one or more features; providing a landmark map comprising a list of landmark representations, wherein each landmark representation is a representation of a respective physical landmark; creating a plurality of policies, each policy comprising at least one matching decision of the feature and the landmark representation, each matching decision having a stage cost; assigning a policy cost to each of the policies, wherein the policy cost is a function of the stage costs of the matching decisions the policy is comprised of; selecting from the collection of the policies the policy with the desired policy cost. There is further provided an assembly having a processor which is configured to carry out said method.

A computer-implemented method includes selecting an image from a set of images, processing the image using a detector portion of a machine learning model to determine a point cluster comprising points representing candidate keypoints and a set of uncertainties, each uncertainty corresponding to a respective point in the point cluster. Includes using the determined point cluster and the determined set of uncertainties to generate a differentiable map representing the point cluster and a spatial variation of uncertainty associated with the point cluster, and processing the differentiable map and a portion of the image using a decoder portion of the model to generate a candidate reconstructed image. Includes updating the model to reduce a loss function comprising a term penalizing a difference between at least one representation of the image and a corresponding at least one representation of the candidate reconstructed image.

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.

To suppress remote control from being continued by an unintended target.SOLUTION: A controller includes: a detection result acquisition unit that acquires detection results provided by a detector that is loaded on a mobile body movable through unmanned operation and detects a landmark located outside of the mobile body; a determination unit that determines the landmark to be detected by the detector; and a control unit that moves the mobile body through unmanned operation and differentiates processes related to the movement of the mobile body between a case where the landmark to be detected by the detector is included in the detection results and a case where the landmark is not included in the detection results.SELECTED DRAWING: Figure 1

A forestry vehicle includes a number of different sensors, including LIDAR. A two-stage SLAM technique is used in combination with landmark trees to generate a tree map. This SLAM technique avoids large loop closures by resolving point cloud data into an arrangement of landmarks, reducing the computational working set to support real-time operations. A variety of services and visualizations are supported by the tree map.

Systems, methods, and / or computer-program products for generating a photo composite are configured to identify a facial photo, assign a community such as a genetic community to the identified facial photo, select and retrieve a subset of photos from a genetic community, determine a landmark in each photo of the subset of photos, compute and apply a similarity transform to each photo based and its landmarks, update estimates of transformed averaged landmarks, calculate a triangularization of the transformed averaged landmarks, warp images to the average image landmarks, and combine the intensities of the warped images. The communities are identified from genetic information. The facial photos are identified from a network of genealogical trees and / or a collection of historical records.

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.

One embodiment of the present invention sets forth a technique for performing landmark detection. The technique includes applying, via execution of a first machine learning model, a first transformation to a first image depicting a first face to generate a second image. The technique also includes determining, via execution of a second machine learning model, a first set of landmarks on the first face based on the second image. The technique further includes training the first machine learning model based on one or more losses associated with the first set of landmarks to generate a first trained machine learning model.

Existing Magnetic Resonance (MR) analysis approaches are either manual, which is time consuming and error prone, or automatic yet fail to identify correct landmarks. The disclosure herein generally relates method and system for MR image analysis. The system trains and uses a Generative adversarial network (GAN) for the MR image analysis. A generator network of the GAN generates a heatmap of a plurality of landmarks, by processing input data. Further, a discriminator network of the GAN, which has gradients updated based on a Modified Discriminator Loss (MDL) calculated based on a foreground pixel loss function and a standard discriminator loss, predicts each of a plurality of patches in a second input data as one of real and fake, based on presence of the one or more foreground pixel regions in the landmark heatmap in the ground-truth heatmap identified based on the foreground pixel loss function.

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.

On a machine (1) adapted to move on a predetermined path (21, 22), a database stores definition data of landmark objects along the path and associated with its 3D geographic coordinates, and a capture block (18) provides an image of a scene in front of the machine (1), said capture block comprising a remote detection block adapted to create an image from received echoes, calculating the direction and the distance of the echo object relative to the machine according to the echo; wherein the steps are repeated: capturing at least one image from the remote detection block; identifying a landmark object (50) in the image according to landmark object definition data in a database; determining the direction and distance of the identified landmark object relative to the machine (1) using the image from the remote detection block; a current 3D position of the machine (1) is determined from the determined direction and distance relative to the machine and the 3D geographic coordinates associated with the identified landmark objects in the database.

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.

A positioning method based on a linear landmark effectively solves the problem of inaccurate positioning of the linear landmark, and comprises the following steps: in an application scene, taking a landmark detected by a robot at each sampling moment as an observer of the robot, and obtaining measurement data of a plurality of groups of landmarks; performing aggregation calculation on the measurement data of the plurality of groups of landmarks to obtain a plurality of aggregation results; tile numbers are added to the multiple aggregation results respectively, and multiple target data points are obtained; and according to an improved cross-ray method, matching the target data point with the contour of the landmark to obtain an updated association table of the landmark and the data file.

According to the invention, on board a machine (1) adapted to move along a predetermined path (21, 22), a database contains object definition data constituting reference points along the path and associated with its 3D geographic coordinates and an acquisition block (18) provides images of the scene in front of the machine (1), said acquisition block comprising a remote sensing block adapted to create an image from received echoes and to calculate, from said echoes, a direction and a distance, relative to the machine, of said reflecting object; these steps being repeated: acquire at least one image from the remote sensing block; identify in said image an object constituting a reference point (50) according to the object definition data constituting reference points in the database;using the image from the remote sensing block, determine a direction and a distance, relative to the machine (1), of the object constituting an identified reference point; determine the current 3D location of the machine (1) as a function of said direction and distance determined relative to the machine and the 3D geographic coordinates associated with the object constituting an identified reference point in the database.

In accordance with an aspect of the present disclosure, there is provided a vehicle position estimation method. The method comprises, obtaining a landmark-based initial position information of a camera by matching a landmark in a high definition map corresponding to a GPS-based initial position information of a vehicle to an image obtained by the camera of the vehicle, and obtaining estimated position information of the vehicle by comparing the image with a landmark in the high definition map corresponding to each of a plurality of candidate position information sampled based on the landmark-based initial position information of the camera.

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