Drone Sensor Fusion for Autonomous Navigation

A redundant flight control and navigation system for drones that ensures high safety and reliability through dual-redundancy architecture. The system comprises multiple sensors, a data fusion and filtering unit, a sensor information determination system, a flight control computer, and a control information determination system, all of which operate in parallel to provide fault-tolerant operation. The system can also interface with multiple actuators to execute control decisions.

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Autonomous drone system for exploration and reconnaissance in unknown environments. The system allows the drone to autonomously fly, map the environment, locate targets, avoid obstacles, and return home. The drone acquires data from cameras, lidar, and IMU to estimate pose, recognize targets, and generate a 3D map. It plans safe paths using ray-casting and sensor fusion. The drone applies the path to fly autonomously. This allows it to explore unknown areas, accurately locate targets, avoid obstacles, and return home.

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Controlling a UAV's flight altitude and posture in environments where satellite signals are poor or blocked, like flying under bridges, by using a secondary barometer to compensate for wind-induced air pressure changes. The UAV's onboard barometer provides the initial air pressure reading. This is synchronized with an external reference barometer's reading and recalculated to account for any wind effects. The compensated air pressure is then fused with other sensor data to determine the target altitude and posture. This allows accurate flight control even when satellite signals are unavailable.

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Facilitating managing of paths for unmanned vehicles using a hierarchical multi-drone/sensor platform with information lateralization and federated path planning. The platform involves multiple drones, ground robots, and sensors with complementary functions acquiring heterogeneous information at different resolutions. This information is integrated and fed back to adjust planned paths for missions. The platform is modeled after brain lateralization, where drones/sensors have specialized roles like human hemispheres.

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A method for calibrating a camera and lidar sensor using a calibration board with known patterns and reflection areas. The method determines the intrinsic camera parameters and then uses the lidar sensor's projected laser lines to detect the calibration board's pose. The calibration board's known patterns and reflection areas enable simultaneous calibration of both sensors without requiring prior camera calibration.

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An autonomous control system for unmanned aerial vehicles (UAVs) that uses machine learning to detect landing spaces from camera images. The system generates pixel-level labels for the images, identifying landing spaces, occupied spaces, and non-landing spaces, and uses these labels to determine the UAV's relative position and control its movements.

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An autonomous mobile object's self-position estimation method is dynamically switched between two modes based on the object's state. When the object is moving, it uses a successive estimation method that integrates internal sensor data. When the object is stopped, it switches to a discrete estimation method that uses external sensor data, enabling more accurate positioning in environments where GPS is unreliable.

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A sensor fusion system for vehicle control applications that integrates visible light cameras with shortwave infrared (SWIR) and longwave infrared (LWIR) cameras to enhance object detection and recognition capabilities in adverse weather conditions. The system determines environmental conditions, such as low light or glare, and automatically switches to alternative sensor modalities to maintain reliable object detection and tracking. The fused images from multiple sensors are processed to generate a comprehensive understanding of the vehicle's surroundings, enabling proactive hazard detection and prediction.

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Autonomous drone navigation control for stable flight in extreme wind conditions using onboard sensors and physics-based modeling. The drone estimates thrust and drag forces from rotor geometry, measures wind, and uses feedforward flight control to compensate for winds. It also has a relationship between RPM and throttle input for rotors. This allows accurate flight path following in severe winds without relying on complex aerodynamic models.

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A navigation system for autonomous vehicles that integrates optical sensor data with motion sensor data to provide accurate positioning in environments where traditional GNSS systems are unreliable. The system uses a nonlinear state estimation technique that incorporates a measurement model for optical samples, enabling direct integration of optical data with motion sensor data to generate a precise navigation solution. The system can operate in environments with limited GNSS visibility, such as dense urban areas, and can also be used in pedestrian navigation applications.

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An information processing device, method, and system for accurate calibration of multiple sensors, including cameras and lidars, regardless of sensor type or range. The system uses a third sensor to measure 3D information of calibration objects placed within the measurement ranges of the first and second sensors, and calculates the relative positions and orientations of the sensors based on the third sensor data and sensor data from the first and second sensors.

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A lightweight pose estimation system for autonomous vehicles that determines vehicle position and orientation using a combination of inertial, kinematic, and odometry sensors. The system generates a pose value by integrating sensor readings and can adjust measurements based on observed noise. It enables efficient and real-time pose estimation for autonomous maneuvers, particularly in emergency braking applications.

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Calibration of an environment sensor system of an infrastructure device, such as a camera system or lidar system, for autonomous vehicle navigation. The system identifies two independent marking elements in the sensor's field of view, determines their relative pose, and compares it to a predetermined target value. Based on the comparison result, the system corrects the sensor's pose information in a reference coordinate system.

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A high-reliability and high-precision navigation and positioning method for drones in GPS-denied environments. The method combines inertial sensor data with binocular camera images and ranging radar measurements to achieve accurate pose estimation. A nonlinear graph optimization algorithm based on a sliding window is used to fuse the sensor data and obtain high-precision pose estimates. The system also includes a loop closure detection module that enables four-degree-of-freedom pose graph optimization when the drone revisits a previously mapped location. The optimized pose data is then packaged into a pseudo-GPS signal that is input to the drone's flight controller for positioning and route planning.

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A method for determining the position of an unmanned aerial system (UAS) using a network of radar nodes. Each node measures the slant distance and elevation angle to the UAS, and transmits its position and azimuthal bounds to neighboring nodes. By combining these measurements, the nodes can solve for the UAS position without explicit measurement of its azimuthal position, providing a unique solution without ambiguity.

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A method for determining a position estimate of a mobile device using visual positioning. The method includes obtaining sensor information, detecting identifiable features in the sensor information, determining a range to the features, obtaining coarse map information, and determining the position estimate based on the range and map information. The method can utilize various sensors, including cameras, lidar, and radar, and can leverage coarse map data from remote servers.

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Autonomous navigation system for unmanned aerial vehicles (UAVs) that enables reliable flight in environments where traditional navigation sensors are unreliable. The system uses a combination of GNSS location signals, inertial measurement unit (IMU) data from accelerometers and gyroscopes, and quadcopter aerodynamics to determine the UAV's position, velocity, and orientation. By leveraging the unique characteristics of quadcopters, the system can maintain stable flight and navigate through challenging environments without relying on cameras, compasses, or magnetometers.

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A method for radar-to-lidar calibration in autonomous vehicles that eliminates the need for specialized calibration targets. The method uses point cloud registration and entropy minimization to align radar and lidar point clouds gathered from different vehicle poses, enabling calibration in unstructured environments. The process aggregates radar and lidar point clouds using vehicle odometry and SLAM data, and then minimizes entropy over multiple drive segments to achieve accurate calibration.

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Automated extrinsic calibration system for lidars, cameras, radars, and ultrasonic sensors on vehicles and robots, comprising a turntable system, calibration target system, and distributed imaging systems. The system enables precise calibration of sensor systems through automated scanning, target configuration, and image capture, eliminating the need for manual measurement and technician expertise.

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An object ranging apparatus for improving accuracy and stability of object ranging in autonomous vehicles, comprising: an object recognition unit; a first distance estimation unit using depth estimation; a second distance estimation unit using motion parallax; and a combining unit that combines the results of the two estimation methods based on factors such as steering wheel angle and acceleration.

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