345 results about "Localization system" patented technology

Systems and methods described herein include implementation of road surface-based localization techniques for advanced vehicle features and control methods including advanced driver assistance systems (ADAS), lane drift detection, passing guidance, bandwidth conservation and caching based on road data, vehicle speed correction, suspension and vehicle system performance tracking and control, road estimation calibration, and others.

A video localization system localizes actions in videos based on a classification model and an actionness model. The classification model is trained to make predictions of which segments of a video depict an action and to classify the actions in the segments. The actionness model predicts whether any action is occurring in each segment, rather than predicting a particular type of action. This reduces the likelihood that the video localization system over-relies on contextual information in localizing actions in video. Furthermore, the classification model and the actionness model are trained based on weakly-labeled data, thereby reducing the cost and time required to generate training data for the video localization system.

A video localization system localizes actions in videos based on a classification model and an actionness model. The classification model is trained to make predictions of which segments of a video depict an action and to classify the actions in the segments. The actionness model predicts whether any action is occurring in each segment, rather than predicting a particular type of action. This reduces the likelihood that the video localization system over-relies on contextual information in localizing actions in video. Furthermore, the classification model and the actionness model are trained based on weakly-labeled data, thereby reducing the cost and time required to generate training data for the video localization system.

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

A localization system comprises: a device; a master unit which wirelessly transmits a first localization signal; a plurality of lateration units distributed about the area within which the device is being localized, wherein each lateration unit of the plurality independently starts its own timer upon its receipt of the first localization signal; and a localization unit. The device receives the first localization signal and responsively wirelessly transmits a second localization signal. Each of the lateration units: independently receives the second localization signal; stops its respective timer responsive to receipt of the second localization signal; and wirelessly transmits a timer count signal to a localization unit. The timer count signal identifies the transmitting lateration unit and a count of its respective timer. The localization unit utilizes the plurality of timer along with respective distances between the master unit and the lateration units to localize the first device via time-of-flight lateration.

A localization system comprises: a device; a master unit which wirelessly transmits a first localization signal; a plurality of lateration units distributed about the area within which the device is being localized, wherein each lateration unit of the plurality independently starts its own timer upon its receipt of the first localization signal; and a localization unit. The device receives the first localization signal and responsively wirelessly transmits a second localization signal. Each of the lateration units: independently receives the second localization signal; stops its respective timer responsive to receipt of the second localization signal; and wirelessly transmits a timer count signal to a localization unit. The timer count signal identifies the transmitting lateration unit and a count of its respective timer. The localization unit utilizes the plurality of timer along with respective distances between the master unit and the lateration units to localize the first device via time-of-flight lateration.

A localization system comprises: a device; a master unit which wirelessly transmits a first localization signal; a plurality of lateration units distributed about the area within which the device is being localized, wherein each lateration unit of the plurality independently starts its own timer upon its receipt of the first localization signal; and a localization unit. The device receives the first localization signal and responsively wirelessly transmits a second localization signal. Each of the lateration units: independently receives the second localization signal; stops its respective timer responsive to receipt of the second localization signal; and wirelessly transmits a timer count signal to a localization unit. The timer count signal identifies the transmitting lateration unit and a count of its respective timer. The localization unit utilizes the plurality of timer along with respective distances between the master unit and the lateration units to localize the first device via time-of-flight lateration.

An object and camera localization system and localization method for mapping of the real world. The mapping can be done in real-time or near real-time to the detection of the real objects by a camera device which is used to capture one or more images of an object. The localization method can be used to generate an object label of the object and a bounding box of the object in the image. The localization method can be used to generate anchor points in real world coordinates of the real 3D space of the object, a cuboid of the object, and a centroid of the cuboid. A virtual 3D map can be generated which includes the location and pose of the real object in the real-world coordinates.

A context localization system provides relevant local context to a user query to reduce hallucinations and / or inaccuracies for a generative AI system. In embodiments, a corpus of data may be accessed to provide relevant local context. A user query may be used to obtain relevant portions of the local data, which may then be summarized and combined with the user query to form an engineered prompt that include the relevant local context. The engineered prompt is then provided to a generative AI system. In some embodiments, the engineered prompt may allow for determining user sentiment. Other embodiments may be described and / or claimed.

Techniques for validating operation of vehicle systems using map data are described herein. The techniques may include receiving sensor data associated with an environment in which a vehicle is operating and generating estimated map data based at least in part on the sensor data. Additionally, stored map data may be received and compared with the estimated map data. If it is determined that an inconsistency exists between the estimated map data and the stored map data, the techniques may include determining whether the inconsistency is attributable to an error associated with the stored map data, the sensor data, or a localization system of the vehicle. As such, one or more specific remedial actions may then be performed based at least in part on determining that the error is associated with at least one of the stored map data, the sensor data, or the localization system.

Systems and methods for vehicle localization are provided for a robotic vehicle, such as an autonomous mobile robot. The vehicle can be configured with multiple localization modes used for localization and / or pose estimation of the vehicle. In some embodiments, the vehicle comprises a first set of exteroceptive sensors and a second set of exteroceptive sensors, each being used for a different localization modality. The vehicle is able to disregard at least one localization modality for a number of different reasons, e.g., the disregarded location modality is adversely affected by the environment, to use less than the full complement of localization modalities to continue to stably localize the vehicle within an electronic map. In some embodiments, a localization modality may be disregarded for pre-planned reasons.

In various examples, three-dimensional (3D) object or feature detection and localization for autonomous and semi-autonomous systems and applications is described herein. Systems and methods are disclosed that use different types of sensors, such as an image sensor and a LIDAR sensor, to determine information associated with objects, such as traffic objects (e.g., traffic signs, traffic signals, traffic markings, etc.). To determine the information for an object, image data is processed to determine a bounding shape associated with the object. The bounding shape is then used to determine a 3D shape, such as a frustum, corresponding to the object. Additionally, points data generated using the LIDAR sensor, such as an occupancy map and / or a point cloud, is processed to identify a portion of the points associated with (e.g., located within) the 3D shape. This portion of the points may then be used to determine the information associated with the object.

The invention relates to the field of biomedicine, and particularly discloses an epileptic focus artificial intelligence positioning system and method based on magnetic resonance and electroencephalography, and the system comprises the following contents: a data collection module is used for synchronously collecting T1 weighted magnetic resonance images and electroencephalogram signals of scalp; the data preprocessing module is used for performing brain tissue segmentation on the magnetic resonance image, obtaining structural features of each brain region, constructing vectors including whole brain structural features, and obtaining magnetic resonance structural feature vectors; artifacts of the electroencephalogram signals are removed, electroencephalogram features of all brain areas are extracted through time-frequency analysis, a matrix containing whole electroencephalogram physiological features is constructed, and the electrophysiological features are obtained; the cross-modal confidence coefficient dynamic evaluation module is used for establishing a bidirectional constraint rule to perform confidence coefficient calibration on the magnetic resonance structure feature vector and the electrophysiological feature; a positioning model construction and training module; a positioning result output module; according to the technical scheme, noise can be reduced during magnetic resonance and electroencephalogram fusion, and the epileptic focus positioning precision is high.

The invention relates to the technical field of material nondestructive testing, in particular to a CFRP wing skin damage positioning system and method based on multi-modal signal processing and Bayesian optimization DSCN, and the method comprises the steps: carrying out permutation entropy analysis and Higuchi fractal dimension analysis on a signal data set received by a sensor; performing time-frequency feature extraction on the sensor receiving signal data set deviating from the reference; performing damage feature screening on the signal data in the effective time period; training the DSCN structure to obtain an initial CFRP wing skin damage positioning prediction model; performing hyper-parameter optimization on the initial CFRP wing skin damage positioning prediction model according to a Bayesian algorithm; and performing damage positioning prediction on a to-be-detected sample according to the final CFRP wing skin damage positioning prediction model to obtain a corresponding CFRP wing skin damage positioning result. According to the method, effective damage features can be accurately and efficiently extracted from complex signals, and the accuracy and efficiency of CFRP wing skin damage positioning are remarkably improved.

A cargo controller is disclosed herein. The cargo controller includes a wireless emergency module (412) including an ultra-wide band radio, the wireless emergency module (412) configured to communicate with a plurality of cargo emergency stations of a cargo handling system using the ultra-wide band radio, a wireless control module (418) configured to communicate with a cargo control station of the cargo handling system, a processor, a memory operatively coupled to the processor. The memory includes instructions stored thereon that, when executed by the processor, cause the processor to receive ultra-wide band radio signals from the plurality of cargo emergency stations, determine a location within the cargo handling system based on the received ultra-wide band radio signals, and transmit the location to the cargo handling system.

An electrical grid fault localization system including: a plurality of current sensors (measurement devices) electrically connected to the electrical grid; a memory for storing a representation of the electrical grid as a plurality of geographical markers, and for storing a plurality of sets of expected time data, each one of the plurality of sets of expected time data associated with each one of the plurality of geographical markers, each member of the set of expected time data based on an expected signal propagation time from, at least one of the plurality of geographical markers to at least one current sensor; a processor communicative with the plurality of current sensors and the memory, the processor configured to receive measured time data of a fault event from, the plurality of sensors to generate a set of fault event time data and to match the set of fault event time data to at least one of the plurality of sets of expected time data and identify in g / outputting at least one matched geographical marker. Methods and computer-readable media directed to electrical grid fault localization are also described.

The invention provides a supply chain cost anomaly AI positioning system based on multi-mode depth tracing, and relates to the technical field of cost anomaly tracing. According to the supply chain cost anomaly AI positioning system based on multi-modal depth tracing, a three-layer architecture is adopted, a dynamic threshold algorithm and a multi-level association map are combined, real-time detection and root positioning of cost anomaly are achieved, and the three-layer structure is data acquisition, dynamic modeling and visual tracing. The system has the advantages that dynamic adaptability is improved, reasonable fluctuation and abnormal risks are accurately distinguished, the system breaks through the rigid limitation of a traditional static rule, the judgment threshold value is dynamically adjusted by fusing market dynamics (such as bulk commodity prices and policy adjustment) and real-time service data, and a supplier-production-logistics-storage association graph is constructed, so that the risk of abnormal risks is accurately distinguished. And the conduction path of the cost abnormity in each link of the supply chain is visually displayed.

The invention discloses a degradation scene-oriented multi-residual fusion laser radar positioning method, which comprises the following steps of: firstly, performing state prediction by adopting an iterative extended Kalman filtering framework and an IMU (Inertial Measurement Unit), and constructing three complementary observation models of a global map matching residual, a local point-to-plane geometry residual and a luminosity residual; secondly, designing a degradation sensing mechanism based on a covariance ellipsoid, representing absolute and relative degradation degrees through a condition number and an information entropy respectively, realizing quantitative evaluation of system observability, and dynamically adjusting fusion weights of observation residuals; meanwhile, a self-adaptive weight strategy based on luminosity Jacobi intensity is introduced; and finally, performing anomaly detection through deviation comparison between the IMU predicted pose and the IEKF estimated pose, inhibiting pose jump, and ensuring continuity of a positioning time sequence. The method effectively overcomes the challenges of geometric constraint deficiency, positioning drift accumulation and the like of the LiDAR positioning system in the geometric degradation environment, does not need to adjust parameters for a specific scene, and improves the precision, robustness and real-time performance of global positioning in the degradation environment.

Systems and methods described herein include implementation of road surface-based localization techniques for advanced vehicle features and control methods including advanced driver assistance systems (ADAS), lane drift detection, passing guidance, bandwidth conservation and caching based on road data, vehicle speed correction, suspension and vehicle system performance tracking and control, road estimation calibration, and others.

Systems / techniques that facilitate explainable confidence estimation for landmark localization are provided. In various embodiments, a system can access a three-dimensional voxel array captured by a medical imaging scanner and can localize, via execution of a first deep learning neural network, a set of anatomical landmarks depicted in the three-dimensional voxel array. In various aspects, the system can generate a multi-tiered confidence score collection based on the set of anatomical landmarks and based on a training dataset on which the first deep learning neural network was trained. In various instances, the system can, in response to one or more confidence scores from the multi-tiered confidence score collection failing to satisfy a threshold, generate, via execution of a second deep learning neural network, a classification label that indicates an explanatory factor for why the one or more confidence scores failed to satisfy the threshold.

The invention discloses a concept for improved position and orientation determination of a plurality of objects each marked with at least one optoelectronically detectable marker, in which holographic optical elements are introduced into two-dimensional flat markers, at which ambient light is reflected back with sufficiently high intensity even at high scattering angles relative to the surface normal of the two-dimensional flat markers.For this purpose, it is proposed to detect light reflected by holographic optical elements (HOE; 21, 22, 23) applied to the markers (20) in an optoelectronic sensor (32) and to generate a two-dimensional reflection image, and to detect, in an image processing device (34), on the basis of the spectrally resolved local intensity distribution of the two-dimensional reflection image detected by the optoelectronic sensor (32), differences between the reflection behavior of the optoelectronically detectable markers (20) of the current position and orientation of the plurality of objects (100; 200) and the reflection behavior of the optoelectronically detectable markers (20) in a reference configuration of the plurality of objects (100; 200), and to detect deviations in the overall configuration of the plurality of objects (100; 200) relative to the optoelectronic detection device (30) by evaluating the detected differences. determine.

A cargo controller is disclosed herein. The cargo controller includes a wireless emergency module including an ultra-wide band radio, the wireless emergency module configured to communicate with a plurality of cargo emergency stations of a cargo handling system using the ultra-wide band radio, a wireless control module configured to communicate with a cargo control station of the cargo handling system, a processor, a memory operatively coupled to the processor. The memory includes instructions stored thereon that, when executed by the processor, cause the processor to receive ultra-wide band radio signals from the plurality of cargo emergency stations, determine a location within the cargo handling system based on the received ultra-wide band radio signals, and transmit the location to the cargo handling system.

In various examples, three-dimensional (3D) object or feature detection and localization for autonomous and semi-autonomous systems and applications is described herein. Systems and methods are disclosed that use different types of sensors, such as an image sensor and a LIDAR sensor, to determine information associated with objects, such as traffic objects (e.g., traffic signs, traffic signals, traffic markings, etc.). To determine the information for an object, image data is processed to determine a bounding shape associated with the object. The bounding shape is then used to determine a 3D shape, such as a frustum, corresponding to the object. Additionally, points data generated using the LIDAR sensor, such as an occupancy map and / or a point cloud, is processed to identify a portion of the points associated with (e.g., located within) the 3D shape. This portion of the points may then be used to determine the information associated with the object.

A vehicle localization system and method includes acquiring, from a first sensor, first data indicative of a first navigation state of a vehicle; acquiring, from a second sensor, second data indicative of an environment in the vicinity of the vehicle; generating, using a processor communicatively coupled to the first sensor and the second sensor, a second navigation state based on matching the second data to a known map of the environment; and, generating, using the processor, a current navigation state based on the first navigation state and the second navigation state.

System, devices, and methods facilitate determining positioning using distributed antenna system with service availability monitoring. Positioning methods include network based methods and handset assisted methods in addition to a monitoring system to report any service outage and possible location information loss. The system provides location information also at handset only level through application or apps and operating systems with online and off-line access to location database data. A combined monitoring system that monitors antenna output power for mobile coverage and service availability helps also in monitoring the availability of the localization system and dynamic update of lookup information. Monitoring system provides also asset tracking and service analytics features for the active or passive distributed antenna system.

A system is provided for localizing an autonomous work vehicle within mapped agricultural environments when GPS may be unavailable. Range sensors capture LiDAR images that are segmented by a trained model into features such as trunks and canopy. Trunk points are clustered to compute centroids, which are used to estimate left and right tree lines and a lane centerline relative to the vehicle. This sensor-derived centerline is compared with georeferenced lane and row geometry stored in a digital map. A particle filter maintains multiple pose hypotheses, propagates them using vehicle odometry, and weights them according to alignment between the lane centerline relative to the vehicle and the mapped lane geometry for the particle. Resampling and averaging of high-weight hypotheses produces an adjusted pose that corrects for lateral and heading errors. The adjusted pose serves as input to a navigation controller that issues steering and speed commands, enabling accurate autonomous operation.

The invention discloses a highway pavement pit slot real-time identification and positioning system based on computer vision, and the method comprises the following steps: obtaining multi-modal sensor data of a to-be-detected pavement, the multi-modal sensor data at least comprising a visible light image; inputting the multi-modal sensor data into a pre-trained target detection model; outputting a detection result containing the target position and category through the target detection model; wherein the target detection model is an improved model based on a YOLO architecture. According to the real-time recognition and positioning system for the highway pavement potholes based on the computer vision, the target positioning accuracy can be comprehensively improved, better adaptability is achieved for positioning of small targets, irregular targets and complex scenes, meanwhile, stable convergence of the model training process can be promoted, negative effects caused by gradient fluctuation are reduced, and the recognition accuracy of the highway pavement potholes is improved. The consistency and reliability of detection results are effectively enhanced, and more solid performance support is provided for target detection tasks in different scenes.

Systems and methods for improved three-dimensional tracking of objects in a traffic or security monitoring scene are disclosed herein. In various embodiments, a system includes an image sensor, an object localization system, and a coordinate transformation system. The image sensor may be configured to capture a stream of images of a scene. The object localization system may be configured to detect an object in the captured stream of images and determine an object location of the object in the stream of images. The coordinate transformation system may be configured to transform the object location of the object to first coordinates on a flat ground plane, and transform the first coordinates to second coordinates on a non-flat ground plane based at least in part on an elevation map of the scene. Associated methods are also provided.

Systems and methods for determining a location based on image data are provided. A method can include receiving, by a computing system, a query image depicting a surrounding environment of a vehicle. The query image can be input into a machine-learned image embedding model and a machine-learned feature extraction model to obtain a query embedding and a query feature representation, respectively. The method can include identifying a subset of candidate embeddings that have embeddings similar to the query embedding. The method can include obtaining a respective feature representation for each image associated with the subset of candidate embeddings. The method can include determining a set of relative displacements between each image associated with the subset of candidate embeddings and the query image and determining a localized state of a vehicle based at least in part on the set of relative displacements.

To specify the position of a movable body even in a situation where it is difficult to specify the position based on the received radio wave intensity of a beacon signal.SOLUTION: The method includes generating reception data including an area ID of an area in which a receiver is disposed, a received radio wave intensity of a beacon signal, and a reception time; performing image analysis on image data of an imaged area to detect the presence or absence of a movable body in the imaged area, and identifying a movable body ID of the movable body when the movable body is detected; generating image analysis data including the area ID of the area, the presence or absence of the movable body, the movable body ID of the movable body when the movable body is present, and the imaged time; and determining the presence or absence of the movable body using the received radio wave intensity and specifying the presence area based on the strength of the received radio wave intensity when the presence area can be specified, and specifying the presence area based on the image analysis data when the presence area cannot be specified.SELECTED DRAWING: Figure 8