612 results about "Sensor fusion" patented technology

This invention is generally related to methods and apparatus that permit the measurements from a plurality of sensors to be combined or fused in a robust manner. For example, the sensors can correspond to sensors used by a mobile, device, such as a robot, for localization and/or mapping. The measurements can be fused for estimation of a measurement, such as an estimation of a pose of a robot.

A system and method for fusing depth and radar data to estimate at least a position of a threat object relative to a host object is disclosed. At least one contour is fitted to a plurality of contour points corresponding to the plurality of depth values corresponding to a threat object. A depth closest point is identified on the at least one contour relative to the host object. A radar target is selected based on information associated with the depth closest point on the at least one contour. The at least one contour is fused with radar data associated with the selected radar target based on the depth closest point to produce a fused contour. Advantageously, the position of the threat object relative to the host object is estimated based on the fused contour. More generally, a method is provided for aligns two possibly disparate sets of 3D points.

The invention provides, in some aspects, improved methods, apparatus and systems for unmanned aerial vehicle (UAV) operation that utilize multiple data links between a UAV and a control station in order to transmit control and actionable intelligence data. Such methods, apparatus and systems can be used, for example, to monitor a selected environment (e.g., an oil field or other terrain/environment of interest). In a related aspect, such data links comprise satellite communication channels.

A method of estimating the navigational state of a system entails acquiring observation data produced by noisy measurement sensors and providing a probabilistic inference system to combine the observation data with prediction values of the system state space model to estimate the navigational state of the system. The probabilistic inference system is implemented to include a realization of a Gaussian approximate random variable propagation technique performing deterministic sampling without analytic derivative calculations. This technique achieves for the navigational state of the system an estimation accuracy that is greater than that achievable with an extended Kalman filter-based probabilistic inference system.

Systems and methods are disclosed for establishing a mobile network in which each mobile unit, such as a car, becomes a node able to receive and transmit a wide variety of information, including for example, traffic conditions, vehicle mechanical/electrical status, interactive game playing information, streaming audio and video, email, and voice mail. In one aspect, multi-sensor fusion technology is used to determine the best value of a monitored variable, for example, the real time locations of each mobile unit, that may be communicated to others via the network. In addition, a new method of traffic control using real time traffic positioning and density data is disclosed. Further, methods and systems for enhancing driver safety are disclosed. In another aspect, the system may use a unique secure dynamic link allocation system to improve the information transfer from one node (mobile unit) to another and to other networks, such as the Internet.

Accurate and reliable techniques for determining a current status of an accessory device in relation to an electronic device are described.

An apparatus for tracking and identifying objects includes an audio likelihood module which determines corresponding audio likelihoods for each of a plurality of sounds received from corresponding different directions, each audio likelihood indicating a likelihood a sound is an object to be tracked; a video likelihood module which receives a video and determines video likelihoods for each of a plurality of images disposed in corresponding different directions in the video, each video likelihood indicating a likelihood that the image is an object to be tracked; and an identification and tracking module which determines correspondences between the audio likelihoods and the video likelihoods, if a correspondence is determined to exist between one of the audio likelihoods and one of the video likelihoods, identifies and tracks a corresponding one of the objects using each determined pair of audio and video likelihoods.

A method and apparatus for providing three-dimensional navigation for a node comprising an inertial measurement unit for providing gyroscope, acceleration and velocity information (collectively IMU information); a ranging unit for providing distance information relative to at least one reference node; at least one visual sensor for providing images of an environment surrounding the node; a preprocessor, coupled to the inertial measurement unit, the ranging unit and the plurality of visual sensors, for generating error states for the IMU information, the distance information and the images; and an error-state predictive filter, coupled to the preprocessor, for processing the error states to produce a three-dimensional pose of the node.

An intrusion protection system that fuses a network instrumentation classification with a packet payload signature matching system. Each of these kinds of systems is independently capable of being effectively deployed as an anomaly detection system. By employing sensor fusion techniques to combine the instrumentation classification approach with the signature matching approach, the present invention provides an intrusion protection system that is uniquely capable of detecting both well known and newly developed threats while having an extremely low false positive rate.

The invention discloses an automatic driving system based on enhanced learning and multi-sensor fusion. The system comprises a perception system, a control system and an execution system. The perception system high-efficiently processes a laser radar, a camera and a GPS navigator through a deep learning network so as to realize real time identification and understanding of vehicles, pedestrians, lane lines, traffic signs and signal lamps surrounding a running vehicle. Through an enhanced learning technology, the laser radar and a panorama image are matched and fused so as to form a real-time three-dimensional streetscape map and determination of a driving area. The GPS navigator is combined to realize real-time navigation. The control system adopts an enhanced learning network to process information collected by the perception system, and the people, vehicles and objects of the surrounding vehicles are predicted. According to vehicle body state data, the records of driver actions are paired, a current optimal action selection is made, and the execution system is used to complete execution motion. In the invention, laser radar data and a video are fused, and driving area identification and destination path optimal programming are performed.

A method that integrates sensor data and video analysis to analyze object motion. Motion capture elements generate motion sensor data for objects of interest, and cameras generate video of these objects. Sensor data and video data are synchronized in time and aligned in space on a common coordinate system. Sensor fusion is used to generate motion metrics from the combined and integrated sensor data and video data. Integration of sensor data and video data supports robust detection of events, generation of video highlight reels or epic fail reels augmented with metrics that show interesting activity, and calculation of metrics that exceed the individual capabilities of either sensors or video analysis alone.

The invention provides a robot navigation method and device based on multi-sensor data fusion. The method comprises the following steps: establishing a map of a total environment according to data acquired by a laser radar sensor and data of an encoder; acquiring the current position of a robot in the map of the total environment in real time according to data acquired by a laser radar sensor, an accelerometer sensor, a gyroscope sensor and a magnetometer sensor, the map of the total environment and the data of the encoder; acquiring a planned route of the robot from the current position to a targeted position in real time according to the map of the total environment and the current position of the robot; and controlling the robot to keep away from barriers during movement by the data which are acquired by the laser radar sensor, a deep camera, an ultrasonic sensor and an infrared sensor and the data of the encoder. On the basis of reasonable utilization of the sensors to realize navigation of the robot, the method is flexibly applied to various scenes, costs are considered, and the autonomous navigation effect can be achieved.

The invention discloses a multi-sensor fusion-based autonomous obstacle avoidance unmanned aerial vehicle system and a control method. The system includes an environment information real-time detection module which carries out real-time detection on surrounding environment through adopting a multi-sensor fusion technology and transmits detected information to an obstacle data analysis processing module, the obstacle data analysis processing module which carries out environment structure sensing construction on the received information of the surrounding environment so as to determine an obstacle, and an obstacle avoidance decision-making module which determines an obstacle avoidance decision according to the output result of the obstacle data analysis processing module, so as to achieve obstacle avoidance of an unmanned aerial vehicle through the driving of power modules which is performed by a flight control system. According to the multi-sensor fusion-based autonomous obstacle avoidance unmanned aerial vehicle system and the control method of the invention, binocular machine vision systems are arranged around the body of the unmanned aerial vehicle, so that 3D space reconstruction can be realized; and an ultrasonic device and a millimeter wave radar in an advancing direction are used in cooperation, so that an obstacle avoidance method is more comprehensive. The system has the advantages of high real-time performance of obstacle detection, long visual detection distance and high resolution.

A total property security system may be implemented to conduct security and surveillance operations. The system includes security operations centers that are connected to one or more sensors and vehicles for collecting and transmitting surveillance data to a database hosted on cloud services. The collected surveillance data is analyzed in order to automatically deploy security measures and/or recommend courses of action using a rules engine that can be configured to client-specific or user-specific security needs. The cloud services can provide a set of application program interface services that can act on the surveillance operations center. Sensor fusion data and other surveillance data can be also transmitted to vetted monitoring service providers on a subscription basis to provide physical security services to the area within the property perimeter. During the subscription period, the selected monitoring service providers can obtain time-based encryption token for accessing surveillance data.

The invention provides a multi-sensor fusion-based indoor positioning method and a system thereof. The method comprises the following steps: using a map creating module to establish an indoor two-dimensional environment map; starting a positioning system to obtain accurate relative positioning data; and starting the positioning system to obtain global positioning data. The purpose of the invention is to rapidly construct indoor two-dimensional environment information by means of indoor two-dimensional modeling, indoor positioning, robot control and visual positioning in order to generate an indoor two-dimensional grid map, the relative offset positioning information and the direction information of a vehicle body are acquired by using an encoder, a gyroscope and a visual sensor, and a laser radar sensor is used to calibrate an accumulated error in order to obtain accurate global positioning information.

A target tracking and sensory fusion system is adapted for use with a vehicle, and configured to observe a condition of at least one object during a cycle. The system includes a plurality of sensors, and a novel controller communicatively coupled to the sensors and configured to more accurately estimate the condition based on sensory fusion. In a preferred embodiment, Kalman filtering is utilized to produce a fused estimate of the object location. The preferred controller is further configured to match each new sensory observation with a track in a track list, and remove the track from the track list, when a matching observation is not determined, during a subsequent cycle.

The present invention provides a comprehensive method for automatically and unobtrusively analyzing the in-store behavior of people visiting a physical space using a multi-modal fusion based on multiple types of sensors. The types of sensors employed may include cameras for capturing a plurality of images and mobile signal sensors for capturing a plurality of Wi-Fi signals. The present invention integrates the plurality of input sensor measurements to reliably and persistently track the people's physical attributes and detect the people's interactions with retail elements. The physical and contextual attributes collected from the processed shopper tracks includes the motion dynamics changes triggered by an implicit and explicit interaction to a retail element, comprising the behavior information for the trip of the people. The present invention integrates point-of-sale transaction data with the shopper behavior by finding and associating the transaction data that corresponds to a shopper trajectory and fusing them to generate a complete an intermediate representation of a shopper trip data, called a TripVector. The shopper behavior analyses are carried out based on the extracted TripVector. The analyzed behavior information for the shopper trips yields exemplary behavior analysis comprising map generation as visualization of the behavior, quantitative shopper metric derivation in multiple scales (e.g., store-wide and category-level) including path-to-purchase shopper metrics (e.g., traffic distribution, shopping action distribution, buying action distribution, conversion funnel), category dynamics (e.g., dominant path, category correlation, category sequence). The present invention includes a set of derived methods for different sensor configurations.

The invention relates to the technical field of gait analysis in biomedical engineering and provides a human gait analyzing method and system based on multi-sensor fusion. The method comprises the steps of filtering sensor signals to eliminate the signal noise error according to human motion features, and eliminating the integral error by means of the improved zero velocity updating algorithm, so that the method and system can be adapted to different walking scenes; fusing multiple sensor data with the Denavit-Hartenberg method, and reducing the leg position calculation error; calculating the step speed, step length, step frequency, walking period and walking track of a tested person accurately during walking after error correction is conducted; establishing a gait database, and conducting statistic analysis on gait data of different tested persons with the quantile regression analysis method. By the adoption of the method and system, gait parameter measurement precision can be improved, and gait parameters of different tested persons are comparable through standardization.

A method and system for fall detection using sensor fusion are disclosed. In a first aspect, the method comprises in response to any of first and second acceleration magnitude thresholds being satisfied, determining whether a height difference before and after impact of a fall satisfies a threshold and whether an angle threshold between an acceleration vector and a calibration vector is satisfied. In a second aspect, the system comprises a processing system and an application coupled to the processing system, wherein the application carries out the steps of the method.

The invention discloses a robot locating method adopting multi-sensor data fusion. On the basis of laser scanning, combination of odometer displacement and a yaw angle of an inertial measurement unit is taken as initial estimation of pose change, then the current pose of a robot is calculated with a scan matching method, and Kalman filtering is performed finally on the basis of two pose calculation results of an odometer and scan matching. The robot locating method adopting multi-sensor data fusion has the advantages that inherent constraints of conventional odometer system location can be eliminated effectively, and influence of accumulated errors on a locating result is reduced; and mismatching results in a matching process can be reduced effectively; displacement change data of the odometer in a short time interval and the robot yaw angle provided by the inertial measurement unit are taken as initial estimation of a laser scan matching process, so that the speed and the accuracy rate of the scan matching process can be increased greatly, and a better matching result can be obtained finally.