AI-Based Drift Reduction for UAV Inertial Navigation

Inertial measurement unit (IMU) system for precision guidance in GPS-denied environments, comprising multiple IMUs with diverse sensor types and ranges, strategically packaged to provide high-bandwidth and full-coverage measurement of angular rate and linear acceleration. The system employs novel packaging and isolation techniques to enhance sensor survivability and performance in high-g shock environments, and incorporates a recalibration module to address sensor error and bias shift caused by gun launch events. The system also features a dynamic sensor configuration capability, enabling handoff between groups of IMUs with varying dynamic range and resolution characteristics to optimize performance in different flight regimes.

Source

A method and system for constraining navigational drift in a munition caused by Inertial Measurement Unit (IMU) bias error during flight of the munition in a constellation of a plurality of munitions in a Global Positioning System (GPS) denied attack. Each munition determines its estimated position and covariance via its navigation system, shares its position and range to other munitions via datalink communication, and constrains navigational drift by compensating for IMU bias error using the shared position and range information.

Source

Initialization method for an integrated navigation system, comprising: acquiring current motion state estimation of the aircraft and sensor measurement values; obtaining motion state estimation values by filtering according to the attributes of the motion state estimation and the sensor measurement values; and initializing a Kalman linear filter by inputting the motion state estimation values if the motion state estimation prediction results converge.

Source

Stabilizing a camera on a gimbal and drone to eliminate vibrations and jitter during high-frequency motion like drone flight. The stabilization system uses an inertial measurement unit on the gimbal to detect the current orientation. A processor calculates the difference between the desired orientation and the current one. It then commands the camera's actuators to move the optical elements by that amount to compensate for the gimbal motion. This allows stable footage on moving platforms without needing fine-tuned gimbal control.

Source

A method and system for constraining navigational drift in a munition caused by Inertial Measurement Unit (IMU) bias error during flight of the munition in a constellation of a plurality of munitions in a Global Positioning System (GPS) denied attack. Each munition determines its estimated position and covariance via its navigation system, shares its position and range to other munitions via datalink communication, and constrains navigational drift by compensating for IMU bias error using the shared position and range information.

Source

Multi-IMU navigation solution that improves the accuracy and high resolution of navigation in a global positioning system (GPS) denied and/or degraded environment. The solution includes an electronic board comprising an upper surface, a lower surface and a plurality of inertial measurement units (IMUs) mounted on at least one of the surfaces, each IMU having a signal and comprising at least one three-axis accelerometer and/or at least one three-axis gyroscope, the IMUs adapted to be coupled together via firmware, a processor adapted to receive the signal from each IMU, and an algorithm comprised in the processor, the algorithm adapted to synchronize the signals from each of the IMUs, calculate a bias and a drift in the signal of each IMU, and to provide a guidance metric representative of the absolute or relative location of a munition guided by the guidance system and based on the signals of each of the IMUs.

Source

Method to improve yaw fusion and convergence speed in unmanned aerial vehicles (UAVs) by combining GPS, IMU, and magnetometer data. The method involves calculating a corrected yaw from IMU and GPS, aligning the magnetometer yaw using GPS and IMU, and realigning the yaw using GPS again. This multiple-step alignment improves convergence in weak GPS and strong magnetic fields.

Source

System for indoor autonomous drone navigation that doesn't rely on GPS. The drone uses multiple sensors (inertial, visual odometry, tag recognition) and an extended Kalman filter to estimate its position and navigate indoors without GPS. It leverages sensor data fusion from cameras, IMU, and tags to improve position estimation.

Source

System for determining the pose of an autonomous vehicle using a consensus-based approach to combine inertial measurement unit (IMU) data from multiple sensors. The system identifies a primary IMU based on consensus determination and uses its measurements in conjunction with other input signals to determine the vehicle's pose. The system also monitors input signals for staleness and implausibility, and dynamically selects a state update algorithm based on the monitoring signal and consensus determination.

Source

A multi-IMU system for precision guidance in GPS-denied environments, comprising a plurality of inertial measurement units (IMUs) mounted on a single electronics board, each IMU comprising a three-axis accelerometer and/or gyroscope. The IMUs are coupled together via firmware and a processor receives their signals to calculate a bias and drift in each IMU's signal. The system provides a guidance metric representative of the absolute or relative location of a munition guided by the system, based on the signals of each IMU. The system exhibits an angular random walk of less than 0.09°/√hour, enabling accurate navigation in GPS-denied environments.

Source

A calibration method for navigation of an unmanned aerial vehicle (UAV) that compensates for zero-point errors in vector sensors. The method continuously collects sensor data, calculates an adjustment quantity based on current and previous measurements, and applies this adjustment to the sensor output to produce accurate heading and posture data. The adjustment quantity is calculated using a weighted estimation of the current and previous measurements.

Source

Autonomous vehicle control system that determines the state of a vehicle by fusing data from multiple sensors, including inertial measurement units (IMUs), and perception sensors. The system identifies and compensates for IMU data offsets by comparing data from multiple IMUs or by correlating IMU data with perception sensor data.

Source

An inertial navigation device error correction system for aerial vehicles enables autonomous flight even in GPS-denied environments. The system uses inter-vehicle communication to calculate the position of one vehicle relative to another, and then uses the differences between the calculated and measured angles to correct the inertial navigation device's bias errors. This approach enables accurate position and attitude determination even when one or more vehicles are malfunctioning or intentionally providing false data.

Source

A method and apparatus for yaw fusion in aircraft, particularly unmanned aerial vehicles (UAVs), that improves the precision and convergence speed of yaw estimation. The method combines GPS, inertial measurement unit (IMU), and magnetometer data to determine a corrected yaw, magnetometer alignment deviation angle, and GPS realignment deviation angle. These values are then fused to generate a precise yaw estimate, enabling accurate attitude control and stable flight in complex environments.

Source

A target state estimation method for unmanned aerial vehicles (UAVs) that achieves high precision without relying on ground plane assumptions or height data. The method combines image recognition, point cloud processing, and extended Kalman filtering to estimate target location, speed, and other states. It uses multiple measurement sources, including image locations and point cloud data, to improve estimation accuracy and robustness.

Source

A method and apparatus for yaw fusion in aircraft, particularly for unmanned aerial vehicles (UAVs), that improves the stability and precision of yaw estimation through secondary complementary filtering. The method combines data from multiple sensors, including magnetometers, inertial measurement units (IMUs), and global positioning systems (GPS), to correct yaw angular velocity and estimate the final yaw angle. The secondary filtering stage compensates for errors introduced by primary filtering, ensuring accurate and stable yaw estimation even during long-term flight or prolonged yaw-angle maneuvers.

Source

Positioning system for vehicles, ships, and aircraft that combines inertial measurement units and GPS data using a novel architecture of multiple Kalman filters that share common sensor data and operate independently, with a fusion module that determines an optimum mean estimate of the system state. The system enables improved accuracy and reliability through redundancy and fault detection capabilities.

Source

Improved navigation system for GPS-denied environments using relative navigation and cooperative positioning. The system employs an extended Kalman filter for autonomous vehicle navigation that incorporates a reset mechanism to prevent accumulation of measurement drift. The filter periodically publishes its pose estimates along with their covariance matrices, enabling back-end optimization using a small amount of data. This approach enables efficient pose graph updates while maintaining accuracy through coordinated reset operations between autonomous vehicles.

Source

A multi-IMU system for providing location and guidance in GPS-denied environments, comprising a miniature package housing multiple low-accuracy IMUs that are fused together to achieve high-accuracy navigation. The system employs a sensor fusion algorithm to combine data from the individual IMUs, enabling the creation of a single high-performance IMU that rivals tactical-grade devices. The system also features a dynamic sensor configuration that can switch between high-dynamic-range/low-resolution and low-dynamic-range/high-resolution modes based on environmental conditions.

Source

Compensating for oscillating attitude disturbances in aerial vehicles caused by rotating payloads, such as LiDAR systems, by predicting and actively counteracting the disturbances through real-time modeling and control of the vehicle's thrusters. The system determines vehicle and payload parameters, calculates a preferred orientation, and generates corrective inputs based on actual orientation feedback to maintain stable flight.

Source