Self-Navigation Systems for Drones

Visual target recognition and positioning method for quadrotor drones using two pairs of binocular cameras to improve accuracy, coverage, and reliability in complex environments. The drone has one pair of binocular cameras at the front and another pair at the rear. This provides 360-degree coverage for comprehensive spatial perception. Real-time target recognition and positioning is achieved using high-performance computing and GPU acceleration. The binocular cameras improve accuracy by resolving blind spots, handling lighting variations, and reducing structured light interference.

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Enabling autonomous aerial navigation for drones in low light conditions without disabling obstacle avoidance. The drone uses a learning model trained on simulated infrared images for obstacle avoidance in night mode. In day mode, the drone still uses the cameras but filters out the infrared data to improve image quality for navigation. This allows the drone to autonomously navigate in low light without relying solely on GPS. It prevents disabling obstacle avoidance or manual navigation in low light environments.

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Visual navigation of unmanned aerial vehicles (UAVs) using liquid neural networks to enable more accurate and responsive autonomous flight in complex environments. The method involves capturing images from the UAV's camera, feeding them through a liquid neural network to process and analyze the environmental information, and using the network's output to generate new desired flight controls. This allows the UAV to navigate through dynamic and unknown conditions more effectively compared to traditional control methods. The liquid neural network is implemented using a software control system with threads for image processing and reasoning. The network runs on a GPU using Tensorflow for accelerated inference.

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Real-time, accurate flight path navigation for drones in complex environments using fused sensor data. The method involves simultaneously obtaining positioning data from GPS and image data from onboard cameras. Preprocessing is done on the data in an edge computing environment using optimization algorithms to reduce complexity and improve speed. Fusion of the preprocessed data is then done with dynamically adjusted weights based on environmental conditions. The fused data is used for obstacle detection and drone navigation.

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Autonomous navigation system for small quadcopter drones in dynamic environments using onboard sensors and processing. The system allows the drone to safely navigate complex environments with moving obstacles without external sensors. It uses a depth camera to create an occupancy grid map of the environment, extracts independent moving obstacles, estimates their motion, and applies an improved collision avoidance algorithm called ORCA to plan a smooth, collision-free trajectory to the target point. The ORCA algorithm generates optimal mutual collision avoidance sets for multiple moving obstacles.

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Obstacle avoidance method for drones that improves safety and autonomy by using machine learning and a hierarchical response strategy. The method involves monitoring flight status, predicting future trajectories using machine learning, and responding appropriately to anomalies based on severity. This allows intelligent obstacle avoidance, optimized flight efficiency, and quick response to emergencies while reducing overreaction to minor anomalies.

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Indoor drone target tracking system using depth cameras and SLAM for following targets in indoor environments where GPS is not available. The system leverages onboard cameras and IMU to track targets without relying on external positioning systems. It integrates target detection, tracking, SLAM, and path planning to enable real-time indoor drone following without GPS.

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Vision-based autonomous landing method for fixed-wing drones near the ground when GPS and inertial navigation fail. The method involves real-time identification and tracking of runway lines and horizons in images, then calculating the drone's pose using photographic geometry to land accurately. This allows autonomous landing with just camera inputs when traditional navigation fails.

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Detecting obstacles ahead of a drone using high-precision maps during flight to allow early obstacle avoidance preparation. The method involves periodically checking within a distance called the status adjustment distance if any obstacles exist based on high-precision map data. If obstacles are found, the drone transitions to obstacle avoidance flight mode. This allows the drone to prepare for obstacle avoidance regardless of user settings since it can detect obstacles earlier using maps instead of radar.

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An unmanned aerial vehicle (UAV) that can autonomously navigate and avoid obstacles indoors or in areas without GPS signals by using lidar scanning and onboard processing. The UAV has a front-mounted lidar sensor that rapidly scans the surroundings to generate dense point clouds. A high-performance onboard processor analyzes the points to calculate the UAV's position and generate obstacle-avoiding flight paths without relying on external GPS. The processor fuses lidar data with GPS when available to provide absolute positioning.

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Multi-sensor fusion obstacle avoidance system for autonomous tilting wing drones that uses multiple sensors like cameras, lidar, radar, and IMU to accurately and robustly detect and avoid obstacles. The system fuses the sensor data to provide complete scene perception and semantic maps for intelligent obstacle avoidance. It combines satellite navigation with local obstacle sensing to avoid issues with single sensor failures or inaccurate positioning. The fused sensor data is used to generate optimal avoidance paths and control the drone's flight speed and heading to safely navigate complex environments.

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Monocular vision unmanned aerial vehicle (UAV) system that enables autonomous obstacle avoidance using only a single onboard camera. The system converts images from the monocular camera into depth maps, constructs a grid map representing the environment, and updates the UAV's flight path in real time to avoid obstacles. This allows obstacle avoidance using just a monocular camera, reducing cost compared to binocular or lidar systems. The UAV has a fuselage with an onboard chip, camera, and visual positioning system. The rotors have motors with antennas, blade protectors, and a power button.

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Indoor and outdoor autonomous navigation for drones using low-cost sensors to overcome limitations of expensive sensors like GPS. The method involves fusing visual, inertial, and distance sensors for position estimation. It uses techniques like YOLOV5 target detection, feature extraction, segmentation, and distance measurement. Global optimization and loop closure detection complete the navigation system. Indoors it uses feature points and UWB for loop closure. Outdoors it uses GPS. This allows reliable indoor/outdoor navigation using cheaper sensors compared to relying solely on GPS.

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Aircraft flying autonomously using GPS or other positioning systems that can improve reliability of autonomous flight with a minimum increase in cost by using external information sources when GPS is unavailable. The aircraft has a processor that switches to using external sources like ground-based systems or nearby landmarks when GPS fails during autonomous flight. This prevents the aircraft from getting lost or unable to land when GPS accuracy degrades.

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Lightweight, low-cost, and fast vision-based autonomous flight system for small drones that allows autonomous navigation and obstacle avoidance in complex environments without external positioning or mapping. The system uses onboard depth cameras, a lightweight computing platform with specific modules for pose estimation, mapping, planning, and control, and runs on a robot operating system. It allows fully autonomous flight in dynamic, unknown, and complex environments using onboard vision sensing and processing instead of external positioning or mapping.

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Autonomous depth recovery and trajectory tracking for UAVs when GPS signals are unavailable. The method involves using a scalable framework for UAVs that can recover depth information and track trajectories using a monocular camera when GPS signals are not available. The framework has two steps: 1) initializing depth estimation using the monocular camera, and 2) tracking the trajectory using monocular sparse SLAM. This allows the UAV to navigate and track paths indoors or in areas without GPS signals.

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Multi-camera fusion UAV navigation and positioning method for indoor environments. The method involves using multiple cameras on a drone to accurately navigate indoors by fusing the pose estimates from different cameras observing passive optical markers. This reduces errors compared to using a single camera and improves positioning. The method involves calibrating the optical markers, determining marker recognition uncertainties, and optimally fusing the camera poses.

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Drone indoor positioning and navigation using BIM data and visual beacons for accurate and efficient indoor drone flight. The drone uses pre-placed visual beacons with known positions indoors to guide flight and locate itself. When obstacles are detected, the flight path is re-planned using the beacons. BIM data is used to identify indoor obstacles and construct safe flight channels. By leveraging BIM and visual beacons, the drone can navigate complex indoor environments with obstacles and avoid collisions.

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A method for stabilizing and navigating an autonomous drone after takeoff using an onboard camera to capture an image of a person or object. The drone determines the distance to the person in the image and hovers stabilized. It then navigates to waypoints using the captured image as a reference point. This allows precise positioning and stabilization without GPS or other external sensors after takeoff. The drone can also continue capturing images of the person to monitor stability and wind conditions before continuing flight.

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Autonomous drone navigation system that allows safe and efficient flight by transitioning between two navigation modes: GPS-based navigation for initial takeoff and stabilization, and vision-based navigation for capturing images and waypoints. The system uses computer vision to analyze initial images and determine drone position. It then hovers the drone, capturing more images to build a 3D map. Once enough data is collected, it switches to vision-based navigation using the 3D map to navigate. This allows precise and stable flight during takeoff and stabilization, then transitions to autonomous vision-based navigation for waypoint following.

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