Low-Signal Navigation for Drones

Enhancing positioning accuracy, reliability, and adaptability of drones used for power grid inspections by combining Beidou satellite navigation with inertial navigation. The method involves fusing data from both systems using an extended Kalman filter to improve positioning accuracy compared to using just Beidou. The filter adapts weighting based on factors like BDS signal quality, INS performance, and environment. This allows reliable positioning even in areas with satellite blockage or when INS errors accumulate. The combined navigation also enables better dynamic response and environmental adaptability compared to relying solely on Beidou.

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High-precision positioning method for unmanned aerial vehicles (UAVs) operating in complex power transmission environments using sensor fusion and multi-sensor data processing. The method involves integrating visual, inertial, and GPS sensors to provide accurate positioning and navigation for UAVs in power transmission line inspection applications. It combines visual odometry from cameras with inertial navigation and GPS to mitigate errors and improve precision. The sensor data is optimized using a factor graph algorithm and sliding window optimizer to fuse and correct the measurements. This provides robust and accurate positioning for UAVs in challenging power line environments with complex geometry and obstructions that can degrade GPS and visual sensing.

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Robust autonomous positioning and mapping for drones in GPS-denied environments using a fusion of UWB and IMU sensors. The method improves drone positioning accuracy indoors and in other GPS-challenged areas by combining UWB range measurements with IMU inertial data. The UWB provides centimeter-level positioning but is prone to interference and terrain effects. The IMU provides high-frequency attitude data to compensate. The sensors are fused using an unscented Kalman filter to estimate pose and map indoors without external GPS.

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Wireless networking positioning system for small drones that enables indoor navigation and positioning using a combination of WIFI, GNSS, UWB, cameras, and barometers. The system allows drones to transition between outdoor GPS and indoor positioning using WIFI-based localization when GPS is unavailable. The drone has two WIFI modules, one for data communication and another for positioning. It also has a GNSS receiver, UWB chip, camera, barometer, and interfaces for cameras, flight controls, power, and barometer.

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Indoor and outdoor autonomous navigation for drones using multi-sensor fusion to overcome limitations of individual sensors like GPS or visual navigation. The method involves a sequence of steps: visual preprocessing, visual state estimation, IMU attitude correction, global optimization, fuzzy loopback detection, and map construction. It leverages low-cost sensors like cameras and IMU for positioning and mapping instead of expensive lidar or depth cameras. Indoor/outdoor loopback detection uses appropriate sensor combinations like UWB/cameras indoors vs GPS/cameras outdoors.

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Dual-mode positioning system for drones using satellite navigation and local reference stations to achieve higher accuracy than satellite navigation alone. The drone has a primary satellite navigation receiver and a secondary communication radio. A ground reference station also has satellite navigation and communication capability. The drone transmits its satellite positioning data to the center. If the center detects the drone near a reference station, the station sends its position and relative position to the center. The center calculates improved drone position using both drone and reference station data.

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Indoor positioning estimation method for drones using extended Kalman filters to provide accurate positioning indoors where GPS signals are weak or unavailable. The method involves collecting distance measurements from multiple indoor reference points using a ranging device on the drone, along with acceleration and angular velocity data from the drone's IMU. An extended Kalman filter is used to calculate the drone's indoor position based on this data.

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Drone positioning and navigation that doesn't rely on GPS satellites, which can be unreliable. The system uses ground base stations located in the drone's flight field. The drone communicates with nearby base stations to obtain positioning signals. If GPS is lost, the drone can still navigate by triangulating signals from the base stations. This provides a backup positioning method that works in environments with poor GPS reception.

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UAV navigation algorithm that improves positioning accuracy during signal interruptions using a NARX neural network and enhanced nonlinear extended Kalman filter (NASRUKF). During normal signal availability, the UAV uses a regular extended Kalman filter (EKF) for positioning. But when signals disappear, the NARX neural network predicts missing positions based on previous measurements. The predicted positions are then fused with the EKF output using the enhanced NASRUKF. This hybrid approach leverages the neural network's prediction capability when signals are lost to maintain better position accuracy compared to just the EKF.

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Navigation system for unmanned aerial vehicles (UAVs) that does not rely on GPS and can work in urban environments with limited satellite coverage. The system uses two synchronized periodic wideband signals transmitted from base stations. The UAV receives the signals and determines its position relative to the base stations based on the signal reception times and intensities. This allows the UAV to navigate along a flight path defined by the base stations without GPS.

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Deploying a visual navigation system for UAVs that provides reliable positioning when GPS signals are unavailable or unreliable. The technique involves placing multiple geo-fiducials around a landing pad, each with a unique offset and direction from a surveyed center point. The UAV uses computer vision to recognize and triangulate the geo-fiducials for precise navigation.

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Visual positioning and navigation for unmanned aerial vehicles (UAVs) using matrix Lie groups and factor graphs to improve accuracy compared to traditional methods. The technique involves fusing visual and inertial sensors to estimate UAV pose, then optimizing and constraining the estimates using Lie groups and factor graphs. This provides accurate visual positioning and navigation for UAVs, especially in tight spaces where GPS signals are weak.

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A UAV positioning system that uses multi-source data fusion to improve the accuracy and reliability of UAV positioning. The system fuses data from multiple sources like onboard sensors, external base stations, and compressed sensing techniques to provide high-precision positioning. It uses a federated filtering algorithm to process and fuse the multi-source data. This involves dispersing the data, globally fusing it, and outputting the final fused position estimate. This allows compensating for errors and uncertainties in individual sensor data to achieve more accurate UAV positioning.

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Using cellular networks to provide real-time navigation assistance to vehicles like drones that lack dedicated navigation systems. The method involves triangulating the drone's position from cell tower signals and using that to determine if navigation assistance is needed. This could be due to conflicts with traffic rules or other factors. If assistance is required, notifications are sent to the drone and other nearby vehicles to guide them. The cellular network acts as a backup navigation system for vehicles lacking one.

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Quadrotor UAV positioning method using multi-sensor fusion for improved robustness and adaptability in different environments. The method fuses data from multiple sensors like IMU, vision, GPS, and ultrasonic to estimate accurate position and attitude. This distributed sensor setup allows better environmental adaptation compared to single sensor approaches. The sensors provide primary state data which is fused using an extended Kalman filter in a loosely coupled manner. This provides stable and accurate position and attitude estimates that can be used by the flight control system.

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Supporting unmanned aircraft to navigate and land safely if communication with the ground station is lost. When the aircraft loses its data link, it conveys its intent to navigate and land via voice communications on the radio frequency assigned for the airspace. The aircraft composes a message indicating its intent to execute a preconfigured lost-link procedure, converts it to speech using text-to-speech, and sends the voice message over the radio channel.

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Seamless autonomous navigation for small unmanned aerial vehicles (UAVs) that allows indoor and outdoor navigation without relying solely on GPS. The method uses a combination of sensors like IMU, magnetometer, barometric altimeter, laser rangefinder, GNSS, and visual odometry. The key idea is to fuse sensor data in a way that enables consistent position estimation regardless of GPS availability. The UAV initially uses GPS for positioning, but if it loses GPS, it continues navigating using other sensors. It converts the fused sensor data into station-centered coordinates, which allows consistent position estimation. When GPS returns, it reverses the coordinates to match the initial GPS location. This provides seamless indoor/outdoor navigation without requiring GPS throughout.

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Global positioning method for unmanned aerial vehicles (UAVs) that allows them to fly and navigate in environments where Global Navigation Satellite System (GNSS) signals are denied or unavailable. The method involves using onboard cameras, inertial measurement units (IMUs), barometers, and magnetometers to estimate the UAV's position and orientation without relying on GNSS. It leverages computer vision to track feature points in images, integrates IMU data for local position, calculates altitude from the barometer, and determines heading from the magnetometer. The fused local estimates are then transformed to global coordinates for complete navigation.

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Method for unmanned aerial vehicle (UAV) positioning when GPS signals are weak or unavailable. The method involves using a combination of inertial navigation and 5G cellular network positioning. The UAV's initial position is estimated by inertial navigation. Then, it uses 5G signals to determine a secondary position. The reliability of the inertial data is checked at intervals. If good, it combines the inertial and 5G data weighted by reliability. If inertial data is unreliable, it fuses both using a Kalman filter to determine the current position. This allows UAVs to maintain accurate positioning in areas with weak GPS by leveraging 5G signals.

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Smooth positioning method for indoor and outdoor drones that enables seamless flight transitions between indoor and outdoor environments. The method uses a GPS fusion visual inertial navigation system that combines GPS, IMU, camera, barometer, and magnetometer data. It dynamically switches sensor fusion modes based on sky openness and GPS quality to maintain consistent positioning indoors and outdoors. The method involves factor graph optimization to fuse the sensor measurements, convert maximum a posteriori estimation to sparse linear optimization, and implement in a factor graph optimization framework.

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