286 results about "Global Positioning System" patented technology

The invention discloses a mobile phone anti-theft system and method based on a camera and a GPS (Global Position System) of a mobile phone. The mobile phone anti-theft system comprises a mobile phone CPU (Central Processing Unit), a microprocessor, a camera driving module, an input module and an unlocking detection module, wherein the input module is connected with the unlocking detection module; the unlocking detection module is connected with the mobile phone CPU; and the mobile phone CPU is connected with the camera driving module through the microprocessor. Compared with the prior art, the mobile phone anti-theft system and method provided by the invention have the advantages as follows: an unlocking operation is carried out when the mobile phone is used after the mobile phone is lost or stolen, and a background operation program which is installed in the mobile phone is triggered and started when the unlocking operation is inaccurate; and a user who currently uses the mobile phone is positioned and photographed through a GPS module and the camera of the mobile phone; meanwhile, obtained information is sent to a mailbox or other addresses which are specified by the user in time, so that the user is convenient to alarm and can find the mobile phone back accurately in time, and the problems of the mobile phone loss and personnel information release of the user can be solved.

The invention discloses an ultra-wideband laser radar inertial navigation cooperative SLAM (Simultaneous Localization and Mapping) method and system, which are suitable for robot positioning and mapping tasks in a GPS (Global Positioning System)-free environment. According to the method, high-frequency motion priori of an IMU, relative pose constraint of LiDAR and absolute ranging information of UWB are fused, a unified factor graph optimization model is constructed, and multi-sensor cooperative positioning is realized; in the system initialization stage, IMU bias calibration, UWB base station coordinate configuration and LiDAR initial attitude estimation are completed; in the operation process, IMU pre-integration is utilized to predict the pose, LiDAR point cloud registration is utilized to obtain the relative motion between key frames, UWB ranging is combined to construct a residual item, a self-adaptive weight mechanism is introduced to suppress ranging abnormity, and the fusion robustness is improved; the system supports geometric feature loopback detection, cross-frame constraints are constructed in combination with UWB to carry out closed-loop optimization, and an optimization result is used for real-time incremental updating of a global map; the method has the advantages of high positioning precision, strong anti-interference capability, wide application scene and the like.

The invention relates to the technical field of aerospace, in particular to low-altitude traffic flow airspace-oriented real-time planning, and provides a centimeter-level precision detection network covering a low-altitude airspace by integrating multi-dimensional data sources such as radar electromagnetic feature recognition, ADS-B (Automatic Dependent Surveillance-Broadcast) automatic monitoring, Beidou or GPS (Global Positioning System) space-time reference positioning and the like. The three-dimensional trajectory and motion situation of the aircraft are solved in real time, intelligent reconstruction of an airspace sector and adaptive optimization of a flight corridor are realized by adopting a dynamic programming algorithm driven by reinforcement learning based on the real-time pose data of the aircraft, and a flight conflict prediction model constructed by combining a space-time convolutional neural network is used for predicting the flight conflict. According to the method, potential risks can be pre-judged, an optimal avoidance path can be generated, full-process digital management and control from identity verification to airspace authorization can be realized by constructing an aircraft digital identity authentication system and a dynamic access control mechanism, and modules such as a three-dimensional navigation information service module, a low-altitude digital communication private network module and an intelligent early warning and warning system module are integrated. And the guarantee of full-life-cycle service is provided for the low-altitude aircraft.

The invention discloses an unmanned aerial vehicle cluster intelligent cooperative positioning method and system based on machine learning, relates to the technical field of artificial intelligence and unmanned aerial vehicles, and aims to solve the problems that positioning signals are attenuated and drifted and the spraying precision is affected due to spray droplets of unmanned aerial vehicles in agricultural plant protection. According to the scheme, multi-source heterogeneous sensor data of a global positioning system, an inertial measurement unit, vision, ultra wide band and the like are acquired through each unmanned aerial vehicle in a cluster and are locally estimated; the central node cooperatively aggregates data, models fogdrop interference such as fogdrops by using a deep learning model, and dynamically corrects sensor noise; according to the method, the positioning robustness, the positioning precision and the self-adaptability are remarkably improved, and the spraying precision in a complex environment is ensured.

The invention discloses a construction site three-dimensional modeling method based on multi-sensor fusion. The method comprises the following steps: synchronously acquiring data by using a laser radar, a camera, an infrared sensor, a GPS (Global Positioning System) and an IMU (Inertial Measurement Unit); the method comprises the following steps of: performing denoising and filtering operation on collected original data, aligning a timestamp and a space of the collected data, fusing the collected multi-source data, and aligning and fusing the processed multi-source point cloud data through a point cloud registration and fusion algorithm to generate a complete three-dimensional point cloud model; generating a grid model by using a surface reconstruction algorithm based on the point cloud data, and performing texture mapping in combination with texture information collected by a camera; the model is simplified and the precision of the model is improved through a model optimization technology, and a reliable three-dimensional visualization foundation for construction management and analysis is formed; real-time detection and modeling of dynamic targets such as mobile equipment and workers on a construction site are realized, and the real-time response capability of the system in a complex construction site environment is ensured through combination of a lightweight neural network architecture and an edge computing technology.

The invention discloses a cross-scale self-supervised spatio-temporal trajectory basic model training method, which comprises the following steps of S1, preprocessing original GPS (Global Positioning System) data to obtain trajectory data of different spatial granularities; s2, encoding the trajectory data of the spatial granularities to obtain trajectory representations corresponding to the spatial granularities; s3, on the basis of the trajectory representation of each spatial granularity, randomly selecting a part of elements to execute mask operation, and calculating the difference between the predicted trajectory data and the real trajectory data as mask reconstruction loss; s4, performing comparative learning training on the positive sample pair and the negative sample pair to construct comparative learning loss; s5, performing dynamic fusion on the trajectory representation of each spatial granularity and the importance weight to obtain a unified representation after trajectory fusion; and S6, performing weighted summation training on the mask reconstruction loss and the contrast learning loss through weight hyper-parameters to obtain total training loss of the model, updating model parameters to convergence by adopting a gradient descent algorithm, and generating a pre-training model.

A client device includes: a display device that includes a touchscreen; a camera; a global positioning system (GPS) device; at least one processor; and a memory that stores a plurality of photographs generated by the camera, application data corresponding to a plurality of applications including at least one application that facilitates storage and selective deletion of the photographs based on subsets of photographs containing images of different people, subsets of photographs taken at different locations and subsets of photographs containing images of particular groups of people.

The invention provides a binocular vision global positioning system and method for a mobile robot, and relates to the technical field of robot vision navigation, and the method comprises the steps: carrying out the adaptive exposure compensation of binocular images of a left camera and a right camera, extracting edge contour feature points irrelevant to illumination, and generating a high-quality depth feature map through the combination of parallax calculation; meanwhile, feature units are constructed according to the inflection points, so that the global position of the mobile robot is determined. Therefore, the problem that the global positioning precision is reduced due to unstable visual feature extraction caused by rapid change of illumination intensity of the mobile robot in a multi-region switching scene can be solved to a certain extent.

A system for forming and maintaining GPS tracked and mapped environmentally-clean chemical-based fire protection zones formed over property and ground surfaces in and about a neighborhood of homes, that proactively inhibit fire ignition and flame spread when exposed to wild fire. The system includes a GPS-tracking anti-fire (AF) liquid spraying system wirelessly connected to a wireless communication network supporting a network database. The GPS-tracked anti-fire liquid spraying system is used to spray particular GPS-specified ground surfaces in and about the neighborhood, with an environmentally-clean anti-fire chemical liquid so to form environmentally-clean chemical fire protection zones over the GPS-specified property surfaces, while generating GPS-coordinate and time / date stamped data records pertaining to the application of the environmentally-clean anti-fire chemical liquid spray during the formation of the environmentally-clean chemical fire protection zones. The GPS-coordinate and time / date stamped data records are transmitted over the wireless communication network for storage in the network database. A mobile computing system is used to access the GPS-coordinate and time / date stamped data records from the network database, relating to the formation of the GPS-specified environmentally-clean chemical fire protection zones, so as to verify that the GPS-specified environmentally-clean chemical fire protection zones were formed at the GPS-specified property surfaces on the particular date, to proactively inhibit fire ignition and flame spread when exposed to wild fire.

Described herein are unmanned aerial vehicles (UAVs) and antenna assemblies thereof for shaping radiation patterns in global positioning system applications. For example, an embodiment pertains to an antenna assembly onboard an aerial vehicle. The antenna assembly includes a radiating element coupled to a chassis of an aerial vehicle and positioned in parallel with the chassis, a substrate coupled to the radiating element, a ground plane coupled to the substrate, and a feed cable coupled to the radiating element through the substrate and the ground plane. The ground plane includes a center portion in parallel with the chassis and with the radiating element, and side portions positioned at an angle relative to the chassis and to the radiating element.

A hydrofoil board having a hydrofoil configured to be automatically controlled to stabilize the board in a level position even when incurring waves. The hydrofoil includes a pair of individually controllable flaps that control the pitch and direction of the hydrofoil board when propelled in motion. A processor uses an inertial measurement unit (IMU) to obtain orientation and acceleration information of the hydrofoil board. A global positioning system (GPS) unit is also used as an additional speed and location sensor. The processor combines IMU data with a user / rider's input, such as selected speed and direction via handheld wireless controller, and individually controls the flap motors to position the flaps, and the propulsion motor to set speed. In one example, the controller is configured to bring the hydrofoil board to a complete and stabile stop.

A high-altitude platform and method for in-flight navigation, the platform comprising a ground-facing camera and a computing device including a memory and a processor, the memory storing instructions which when processed cause the computing device to perform a position determination method, comprising: acquiring one or more images from the ground-facing imaging device; retrieving, from an image dataset associated with a trained model, a plurality images associated with an area of interest; processing, using an image comparator, the acquired one or more images and the retrieved images associated with terrain of interest to derive therefrom respective acquired image feature sets; using a loss function, comparing each acquired image feature set to each retrieved image feature set to identify for each acquired image a corresponding matching retrieved image; using the matched image and the optical characteristics of the ground-facing camera, determining the position of the platform.

A method for labeling vehicular sensor data includes accessing sensor data captured by a plurality of sensors disposed at a vehicle. The method also includes accessing global positioning system (GPS) sensor data of the vehicle. For each time step of a plurality of time steps associated with the sensor data, vehicle position information is extracted from the GPS sensor data that represents a respective geographic location of the vehicle at the respective time step. For each time step of the plurality of time steps of the recording of sensor data, respective location-based information associated with the respective geographic location of the vehicle at the respective time step is obtained from a database. For each time step of the plurality of time steps of the recording of sensor data, the recording of sensor data at the respective time step is labeled using the respective obtained location-based information.

The invention relates to the technical field of dam termite prevention and control in water conservancy and hydropower engineering, in particular to a dam termite nest positioning method and detection equipment based on intelligent multi-mode fusion, which utilizes technologies such as geological radar, infrared thermal imaging, RTK (Real Time Kinematic) and GPS (Global Positioning System) positioning and is assisted by a machine learning algorithm to carry out data processing. An infrared thermal imaging camera carried by a crawler chassis is used for rapidly scanning the surface of a dam and finding out temperature abnormal points which may be termite activity areas, an underground high-resolution image is further obtained through a carried ground penetrating radar, meanwhile, coordinate information of the areas is collected, a high-precision dam area map is combined, and the termite activity area is obtained. The termite nest position is further accurately confirmed; by means of the method, the accuracy and efficiency of dam termite nest positioning can be remarkably improved, meanwhile, the environmental pollution risk caused by a traditional method is reduced, and the method is beneficial to protection of safe and stable operation of a water conservancy project and makes a contribution to environmental protection.

The invention is suitable for the field of farmland weed treatment, and provides an unmanned vehicle-mounted remote sensing detection-based farmland early weed identification method, which comprises the following steps of: constructing an unmanned vehicle-mounted remote sensing detection system and acquiring a multispectral image, a thermal infrared image and GPS (Global Positioning System) data; data transmission and preprocessing of the multispectral image and the thermal infrared image are carried out; carrying out weed recognition on the preprocessed image by using a weed recognition algorithm; experiments prove that the weed recognition result method greatly improves the monitoring efficiency, is especially suitable for large-scale farmland, reduces herbicide abuse, and gives consideration to both production benefits and environmental protection. The technology can effectively reduce resource competition between crops and weeds, directly improve the yield and quality, and reduce manpower and pesticide cost at the same time. And the data can be integrated into a precision agricultural system to promote the implementation of technologies such as variable fertilization and intelligent irrigation. In addition, the flexibility of the unmanned system enables the unmanned system to adapt to complex terrains, and a new solution is provided for organic agriculture and breeding test fields.

Provided is a smart stroller including a stroller, master control module and power control module. The stroller includes a chassis support, a front wheel support which two front wheels are disposed at, a rear wheel support which two rear wheels are disposed at, a seat, handle, guardrail and canopy. The master control module is disposed on a base of the handle of the stroller. The master control module includes a controller, brake controller, first wireless communication device, and global positioning system. The power control module is disposed at the bottom of the rear wheel support and includes a second wireless communication module, power source module, foot brake component, drive motor, deceleration mechanism, and drive controller. The smart stroller is applicable to a global positioning system and an anti-theft system and capable of performing video surveillance, automatic braking, music playing and storytelling to ensure user safety and captivating user experience.

The present application refers to a process and a system for collecting, storing, analyzing and visualizing electrocardiographic (ECG) data collected by portable ECG device (100) with embedded wireless communication technology, additionally containing a portable ECG device (100), a hermetically sealed box (200) of medicines containing a lock system comprising a lock (220), a first device (310) to be accessed by the patient, a second device (320) to be accessed by the medical professional, a platform for graphical display of ECG data, and a remote data storage and processing central. The hardware board of the portable ECG device contains wireless communication technology, global positioning system (GPS), start button (141), rechargeable battery charging port, and an equipment configuration and programming port. The battery (120) is Li-Ion type, containing LEDs indicating device operation, and waiting for connection. The battery status is displayed on the app display. In one embodiment, an electrode (130B) is positioned on the bottom cover (110B) of the housing (110A, 110B, 110C) of the ECG device (100).

The invention discloses a transport case real-time positioning method and device based on multi-sensor fusion, and belongs to the technical field of Internet of Things and intelligent logistics. The method comprises the following steps: acquiring positioning parameters and motion state parameters of a transport case in real time through sensors such as a GPS (Global Positioning System) and an IMU (Inertial Measurement Unit), and carrying out noise filtering, timestamp synchronization and coordinate system unified preprocessing operation on data; an extended Kalman filtering and particle filtering mixed model is adopted, and a weight coefficient is dynamically adjusted according to the GPS confidence coefficient and the IMU confidence coefficient, so that adaptive data fusion is realized; transmitting a positioning result through 4G / 5G and LoRa dual-mode communication, and judging reliability based on a residual error to trigger calibration or switch a positioning mode; according to the method, the problems of insufficient precision, poor robustness and limited real-time performance of a single sensor in a complex environment are solved, centimeter-level positioning precision in the complex environment is realized, the anti-interference performance of positioning is improved, the real-time performance and low power consumption of data transmission are ensured, and a fusion result is prevented from being polluted by abnormal data.

A work vehicle including a chassis, a ground-engaging mechanism, an input device, a global positioning system, and a controller. The input device providing a bench surface. The global positioning system configured to provide a chassis heading signal, a chassis inclination signal indicative of a main fall angle, and a chassis roll signal indicative of a cross slope angle. The controller configured to receive the chassis heading signals, record the chassis heading signal from a first location to a second location; use the recorded chassis heading signal as a reference chassis heading signal; receive a current chassis heading signal; determine an orientation error based on the reference chassis heading signal and the current chassis heading signal; and send a command to actuate the ground-engaging mechanism to shift the current heading to align with the reference heading based on the orientation error.

The invention discloses an unmanned aerial vehicle real-time modeling method and system for a power transmission line disaster scene, relates to the technical field of power operation and maintenance, and aims at a global positioning system signal missing scene. The problem that the sub-graphs are difficult to align when the pose drifts and the common view is weak in the traditional method is solved. A global pose optimization model fuses historical constraints of preprocessing training data, a single-machine positioning result of local pose data and visual features of an operation key frame image set, an internal constraint system without external positioning signals is constructed, pose errors are corrected, strong dependence on an image common-view area is broken through, and the positioning accuracy of the operation key frame image set is improved. Multi-unmanned aerial vehicle pose stability and local data efficient fusion are achieved, early-stage data preprocessing, pose resolving and later-stage three-dimensional reconstruction are combined, the real-time dense map generation period is shortened, support is provided for fault positioning, damage assessment and emergency repair scheme making after power transmission line disasters, and the emergency response efficiency and reliability are improved.

The invention relates to the technical field of computer vision and three-dimensional reconstruction, in particular to a large-scene three-dimensional reconstruction method based on adaptive partitioning and geometric optimization, which comprises the following steps: acquiring an original image set without a GPS (Global Positioning System) tag to construct a weighted image association graph; performing splitting processing on the weighted image association graph by adopting a self-adaptive partitioning method to obtain a plurality of blocks; independently executing Colmap on each block to obtain local sparse point clouds and camera parameters, and unifying all the local sparse point clouds to a global coordinate system by adopting a Umeyama algorithm to form a complete sparse point cloud; a complete sparse point cloud is adopted to pre-train the 3DGS model, and reliable Gaussian center points are screened to form a stable point cloud; constructing low-frequency shape constraint loss, and performing weighted optimization on the low-frequency shape constraint loss, color loss and structure loss to obtain a pre-trained 3DGS model; obtaining a large-scene three-dimensional reconstruction result through the optimized 3DGS model; the method has good adaptability to geometric structure dominated scenes, and the engineering feasibility and efficiency of large-scale scene reconstruction are effectively improved.

The present disclosure generally pertains to systems and methods for autonomously detecting and correcting anomalies in position information provided to aircraft using radio-frequency signals. By enabling autonomously detecting and correcting for anomalies in the operation of a ground-based solution entirely independent of GPS, systems of the present disclosure can make the provided position information more accurate and robust, thereby enhancing the effectiveness and safety of navigation systems using the provided position information. More precisely, systems of the present disclosure may employ a series of ground-based beacon transmitters to provide radio-frequency (RF) signal pulse with a highly regular frequency. A locating receiver can detect the arrival times of these pulses and use this information to detect and report certain anomalies. These reports may then be used to autonomously correct the detected anomalies.

The invention relates to the technical field of unmanned aerial vehicle cable intelligent routing inspection, and discloses a GPS-free unmanned aerial vehicle cable autonomous routing inspection method based on laser radar SLAM, and the method comprises the steps: completing the initialization setting of a system through carrying a laser radar, an inertial measurement unit and an auxiliary sensor; starting a laser radar SLAM (Simultaneous Localization and Mapping) algorithm in an environment without GPS (Global Positioning System) signals to carry out real-time positioning and map construction, and generating an environment point cloud map; planning an autonomous inspection path and optimizing a flight path based on the point cloud map and the preset geographic information of the cable line; controlling the unmanned aerial vehicle to fly along the path, and updating the position in real time by using an SLAM algorithm to compensate the positioning error; synchronously acquiring laser point cloud data, visible light images and infrared thermal imaging data of the cable; identifying a cable structure and an abnormal state through multi-modal data fusion processing; and finally generating an inspection report containing the health condition and the abnormal position of the cable and transmitting the inspection report to a monitoring center. The continuity and coverage integrity of the routing inspection mileage are improved.

In some implementations, a system for video calling may include memory; and one or more processors, wherein the one or more processors are configured at least to receive, from a first user device, a request to search for users in a geographical area; determine that one or more user devices are located in the geographical area; receive, from the first user device, a request to video call a second user device of the one or more user devices located in the geographical area; send, to the second user device, the request to video call the second user device; receive, from the second user device, a video stream generated by the second user device; and send, to the first user device, the video stream generated by the second user device.

The invention relates to the technical field of unmanned aerial vehicles, provides an unmanned aerial vehicle fusion height estimation method, an unmanned aerial vehicle and a medium, and aims to solve the problem that a stable and credible height estimation value cannot be provided due to IMU measurement errors when both RTK and a barometer fail. The method comprises the following steps: performing high-frequency noise filtering on the original acceleration of the inertial measurement unit to obtain a first acceleration value; performing first-order difference on the speed of the global positioning system to obtain an acceleration compensation value; constructing a state space model containing a vertical speed, an acceleration and an accelerometer zero offset drift error; the first acceleration and the acceleration compensation value are fused through Kalman filtering, and drifting is estimated and stripped in real time; and outputting the smooth height by using trapezoidal integral. The device can output a whole-course continuous height estimation value with the drift distance smaller than 0.3 m / min and the bandwidth larger than or equal to 2 Hz, and is suitable for stable flight and uninterrupted operation when both the RTK and the barometer fail in complex scenes such as agricultural plant protection.

The invention discloses a gas pipe network leakage point accurate positioning method based on a multi-source signal attenuation model, and belongs to the technical field of gas pipe network safety monitoring. The method comprises the following steps: arranging monitoring nodes integrating pressure waves, sound waves and an optical fiber vibration module according to a triangular coverage principle, and synchronously acquiring signals through a GPS (Global Positioning System) or the Ethernet; performing targeted denoising on the three types of signals by adopting a wavelet threshold method, a self-adaptive noise cancellation method and an empirical mode decomposition method; constructing a single signal attenuation sub-model associated with the fuel gas viscosity, the density, the pipe diameter and the sound wave dominant frequency, and fusing the single signal attenuation sub-model into a unified attenuation model through information entropy dynamic weight; solving a leakage point coordinate enabling the error between the theoretical value and the measured value to be minimum by utilizing a gradient descent method; through actual coordinate verification, if an error is greater than 5 meters, attenuation coefficient iterative optimization is adjusted. The system adapts to different pressure grades, gas types and complex working conditions, has the advantages of being high in anti-interference capacity and low in operation and maintenance cost, and is suitable for daily operation and maintenance and emergency disposal of the pipe network.

The invention relates to the field of multi-unmanned aerial vehicle cooperative control, and discloses an unmanned aerial vehicle distributed pure azimuth formation control method without a global reference system, which comprises the following steps: designing a distributed preset time attitude estimator for each follower unmanned aerial vehicle, and using local communication information to estimate the attitude of each follower unmanned aerial vehicle; quickly converging the attitude estimation to a leader reference attitude within the time set by the user; a distributed pure azimuth formation control law is constructed, a finite time differentiator is introduced to estimate the change rate of azimuth errors, and direct measurement of azimuth derivatives is avoided; and generating control input according to the estimated attitude and the relative azimuth information under the local coordinate system. Through a specific robust gain design, the formation can still maintain an expected structure and realize accurate tracking under the condition that external disturbance and a time-varying expected trajectory exist. According to the invention, the dependence on an external sensor and a global positioning system is reduced, and the autonomy, robustness and implementability of the unmanned aerial vehicle formation in a complex environment are improved.

Collision / fall reporting and alert systems and methods for micromobility vehicles include at least one proximity sensor, a speed sensor, at least one tilt sensor, a global positioning system, at least one warning device, a communication device, and a controller in communication with the at least one proximity sensor, the speed sensor, the at least one tilt sensor, the global positioning system, the at least one warning device, and the communication device.

A marine drone system utilizing an unmanned aerial vehicle to provide visual feedback for conditions including temperature, depth, and conditions which may suggest favorable fishing conditions, such as weed lines, flotsam, breaks, and objects, such as birds or fish. The system utilizes a plurality of sensors, including, but not limited to, cameras, laser, GPS, radar, and LIDAR. The visual feedback may be shown as a video fees or a map, wherein the feedback is shown as a visual backgrounds, wherein an overlay of interactive functions provides information regarding the conditions. The system also includes method steps for implementing, obtaining, and displaying the information. The system hardware includes the unmanned aerial vehicle, a base station, and a hardwired tether between the unmanned aerial vehicle and the base station providing power and bi-directional data transfer.

The application provides a method and system for detecting camouflaged targets by unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The system ensures the synchronization of UAVs and UGVs through the global positioning system (GPS), and uses the infrared cameras, binocular cameras, and radar sensors carried by both to efficiently collect data in the target area. The system further preprocesses and analyzes the collected data, and uses improved SSD algorithms and pattern recognition techniques to identify potential camouflaged targets, and performs pattern matching and information fusion to improve the accuracy of the identification results. The SE attention module is introduced innovatively, which optimizes the feature fusion process, enhances the network's spatial correlation capture ability, and enables the system to use global information more effectively, improving the detection accuracy of camouflaged targets. The application solves the problem of poor reliability in traditional methods for detecting camouflaged targets, improves the real-time performance and effectiveness of the cooperative operation of UAVs and UGVs, and significantly improves the accuracy and response speed of camouflaged target identification in battlefield environments. It has important theoretical value and application prospect in various military crisis response.