Inertial Navigation System (INS) for Drones/UAVs

Improved control system for drones carrying loads that accounts for the complex motion of the load and drone during flight. The system uses a non-linear filter to fuse sensor data from the drone and load to accurately track the dynamics of the drone-load system. This allows more stable and predictable flight behavior when carrying heavy loads or suspended loads that can swing. The filter model includes parameters like mass, length, inertia, center of mass, and impulse forces from the load and drone thrusters.

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Controlling unmanned systems like drones using motion of a handheld device or weapon instead of a remote control. The unmanned system maneuver controller (USMC) attaches to a weapon or observation device and uses sensors like an inertial navigation system (INS) to track its motion in 3D space. Flight instructions are generated based on the weapon's movement to pilot the unmanned system. This allows precise, remote-less control of the drone by just moving the weapon. The controller can be mounted to a weapon or observation device, such as a rifle or scope, and the motion of the device in 3D space controls the drone's flight.

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Calibrating the navigation sensors of an unmanned aerial vehicle (UAV) to improve accuracy by compensating for zero-point errors. The method involves collecting reference data during two measurements of a known vector with the UAV's vector sensor. The zero-point offset of the sensor is calculated from this data. Then, the original vector measurements are adjusted using the calculated offset to obtain corrected vectors. The UAV's navigation is controlled using the corrected vectors instead of the uncorrected sensor output. This eliminates zero-point errors from the sensor measurements.

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Navigation device for an optical system that determines attitude with reduced drift by combining inertial measurements with visual feedback. The device continuously captures images of the scene and compares them to stored reference signatures to determine angular offsets, which are then combined with inertial data to calculate the navigation attitude. This approach enables precise attitude determination without the need for frequent calibration operations, making it suitable for long-duration observation missions.

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Aerial vehicles that operate in indoor environments require innovative positioning and navigation solutions. The invention addresses the challenge of determining precise altitude within confined spaces by integrating sensors that can accurately measure floor-to-ceiling distances, even in environments with minimal pressure variations. The system employs a unique architecture that enables precise positioning through a combination of LIDAR, time-of-flight sensors, and camera-based imaging. The sensors are integrated into the vehicle's housing, which is designed with a low profile and optimized for vertical movement. This configuration enables the aerial vehicle to maintain stable flight while navigating through narrow indoor passageways and avoiding obstacles. The system enables precise positioning and navigation within indoor environments, addressing the unique challenges of indoor operations.

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Improving SLAM accuracy through sensor coordination. The system automatically switches between absolute and local coordinate systems based on the relative motion of the moving object and the robot. This enables precise positioning even when the robot moves while maintaining a stationary reference frame. The system integrates sensor data from LiDAR, cameras, and IMUs to achieve accurate SLAM positioning.

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A system for precision landing of unmanned aerial vehicles (UAVs) in GPS denied conditions using ground-based radios instead of GPS. The system involves two ground radios each equipped with two-way timing and ranging capability. The UAV receives signals from both radios and triangulates its location with respect to the radios. It then executes a landing procedure at the triangulated location. The UAV has onboard sensors like barometer, radar, laser altimeter, IMU, air data system, and magnetometer for vertical measurement and navigation. The ground radios can provide ground level altitude. The UAV can also perform an acquisition orbit to improve accuracy by expanding the geometry with respect to the radios.

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The growing use of Unmanned Aerial Vehicles (UAVs) raises the need to improve their autonomous navigation capabilities. Visual odometry allows for dispensing positioning systems, such as GPS, especially on indoor flights. This paper reports an effort toward UAV autonomous navigation by proposing a translational velocity observer based on inertial and visual measurements for a quadrotor. The proposed observer complementarily fuses available measurements from different domains and is synthesized following the Immersion and Invariance observer design technique. A formal Lyapunov-based observer error convergence to zero is provided. The proposed observer algorithm is evaluated using numerical simulations in the Parrot Mambo Minidrone App from Simulink-Matlab.

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Abstract For unmanned aerial vehicle (UAV) navigation in global satellite navigation system (GNSS)-denied environments, an image matching-based visual-inertial integrated navigation system is proposed. Deep learning-based methods are used for image matching to address the challenges of multi-modal image matching. A feature mismatch removal method using reference visual data and inertial navigation prior pose is proposed to improve the accuracy and robustness of image matching. Error-state Kalman filtering (ESKF) is applied to fuse the outputs of visual navigation and inertial navigation and calibrate the inertial navigation system. In addition, an image mismatch detection method based on Kalman innovation detection is applied to avoid severe errors caused by image mismatch. Finally, the proposed integrated navigation system is validated by Airsim simulation and a public dataset.

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Over the last years, a rapid evolution of unmanned aerial vehicle (UAV) usage in various applications has been observed. Their use in indoor environments requires a precise perception of the surrounding area, immediate response to its changes, and, consequently, a robust position estimation. This paper provides an implementation of navigation algorithms for solving the problem of fast, reliable, and low-cost inventorying in the logistics industry. The drone localization is achieved with a particle filter algorithm that uses an array of distance sensors and an inertial measurement unit (IMU) sensor. Navigation is based on a proportionalintegralderivative (PID) position controller that ensures an obstacle-free path within the known 3D map. As for the full 3D coverage, an extraction of the targets and then their final succession towards optimal coverage is performed. Finally, a series of experiments are carried out to examine the robustness of the positioning system using different motion patterns and velocities. At the same time, various ways of traversing the environment are examine... Read More

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Method for calculating inertial navigation data that provides a certified navigation solution with a guaranteed error limit, independent of hybrid navigation values. The method combines inertial measurements with external data and behavior models to calculate hybrid navigation values, while simultaneously calculating certified navigation values with a guaranteed error limit. The certified navigation values are calculated using a separate error model that provides a statistical limit of the geolocation data with a probability of having a navigation error outside of this limit, compatible with safety requirements.

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The article discusses the comparison of sensor parameters of selected inertial measurement units (IMUs) used in control modules of small unmanned aircraft (UAVs). The comparison of stochastic parameters of individual IMU sensors was conducted using the Allan variance, and deterministic parameters were evaluated using the six-position method. The module was selected for the given analysis MNAV100CA from Crossbow and Pixhawk PX4 autopilot. The comparison was based on measured data during static measurements. The level of errors in sensors can disrupt or reduce the stability and controllability of the UAV in autonomous control mode. The analyzed parameters of individual sensors can subsequently be used for designing a suitable integration architecture using Kalman filters to eliminate noise processes from sensor measurements in the control process.

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The pure inertial navigation system, crucial for autonomous navigation in GPS-denied environments, faces challenges of error accumulation over time, impacting its effectiveness for prolonged missions. Traditional methods to enhance accuracy have focused on improving instrumentation and algorithms but face limitations due to complexity and costs. This study introduces a novel device-level redundant inertial navigation framework using high-precision accelerometers combined with a neural network-based method to refine navigation accuracy. Experimental validation confirms that this integration significantly boosts navigational precision, outperforming conventional system-level redundancy approaches. The proposed method utilizes the advanced capabilities of high-precision accelerometers and deep learning to achieve superior predictive accuracy and error reduction. This research paves the way for the future integration of cutting-edge technologies like high-precision optomechanical and atom interferometer accelerometers, offering new directions for advanced inertial navigation systems and ... Read More

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Stabilizing flight of an aerial vehicle with movable propulsion units to prevent fluttering and resonance issues when actuators fail. The method involves using the thrust force itself as a manipulated variable to control the propulsion unit's position and orientation. If actuators and propulsion fail, the thrust force still provides feedback to stabilize. Alternatively, the propulsion unit's motion is used to control mounting structure deformation. This closed-loop control prevents fluttering and resonance without relying on actuators.

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This study proposes an enhanced integration algorithm that combines the magnetic field-based positioning system (MPSMagnetic Pose Estimation System) with an inertial system with the advantage of an invariant filter structure. Specifically, to mitigate the nonlinearity of the propagation model and perturbing effect from the estimated uncertainty, the formulation of the invariant Kalman filter was derived in detail. Then, experiments were conducted to validate the algorithm with an unmanned vehicle equipped with an IMU and MPS receiver. As a result, the navigation performance of the IEKF-based inertial and magnetic field integration system was presented and compared with the conventional Kalman filter results. Furthermore, the convergence and navigation performance were evaluated in the presence of state variable initialization errors. The findings indicate that the inertial and magnetic field coupled with the IEKF outperforms the typical KF approach, particularly when dealing with initial estimate uncertainties.

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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.

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The challenge of precise dynamic positioning for mobile robots is addressed through the development of a multi-inertial navigation system (M-INSs). The inherent cumulative sensor errors prevalent in traditional single inertial navigation systems (INSs) under dynamic conditions are mitigated by a novel algorithm, integrating multiple INS units in a predefined planar configuration, utilizing fixed distances between the units as invariant constraints. An extended Kalman filter (EKF) is employed to significantly enhance the positioning accuracy. Dynamic experimental validation of the proposed 3INS EKF algorithm reveals a marked improvement over individual INS units, with the positioning errors reduced and stability increased, resulting in an average accuracy enhancement rate exceeding 60%. This advancement is particularly critical for mobile robot applications that demand high precision, such as autonomous driving and disaster search and rescue. The findings from this study not only demonstrate the potential of M-INSs to improve dynamic positioning accuracy but also to provide a new rese... Read More

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To implement quick and accurate coarse alignment on a smart unmanned aerial vehicle (UAV), we propose an in-motion coarse heading alignment method using the low-cost microelectromechanical system (MEMS) inertial navigation system (INS) aided by the global navigation satellite system (GNSS) in this article. The principle of the proposed method is quite straightforward in that the initial heading can be achieved using trajectory matching: MEMS INS conducts INS mechanization to obtain a precise relative trajectory of UAV in a very short period; meanwhile, the absolute trajectory of UAV is provided by GNSS positioning. The heading can be achieved by comparing these two trajectory vectors. The proposed method is verified on the UAV under different representative motion modes. Results show that the initial heading of UAV can be determined accurately to 2.2 at a 95% confidence level within 4 s.

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Search and rescue operations require mobile robots to navigate unstructured indoor and outdoor environments. In particular, actively stabilized multirotor drones need precise movement data to balance and avoid obstacles. Combining radial velocities from on-chip radar with MEMS inertial sensing has proven to provide robust, lightweight, and consistent state estimation, even in visually or geometrically degraded environments. Statistical tests robustify these estimators against radar outliers. However, available work with binary outlier filters lacks adaptability to various hardware setups and environments. Other work has predominantly been tested in handheld static environments or automotive contexts. This work introduces a robust baro-radar-inertial odometry (BRIO) m-estimator for quadcopter flights in typical GNSS-denied scenarios. Extensive real-world closed-loop flights in cities and forests demonstrate robustness to moving objects and ghost targets, maintaining a consistent performance with 0.5 % to 3.2 % drift per distance traveled. Benchmarks on public datasets validate the sys... Read More

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With the development of rotation modulation, dual-axis rotational inertial navigation systems (RINS) are gradually applied to unmanned aerial vehicles (UAV) to cope with the increasing prominence of GNSS spoofing. The urgent need for autonomous navigation has promoted a popular topic of improving RINS accuracy. However, few have delved into error excitation and propagation from the perspective of carrier maneuvering. Starting from the error phenomena in a UAV flight experiment, this paper studies the error propagation mechanism of gyro installation error in dual-axis RINS under coordinated U-turn. Specifically, the coupling between the control strategies of the dual-axis RINS and the UAV typical maneuvers is revealed. On this basis, the error propagation mechanism for the gyro installation error is derived. Simulations validate the analyzed theory and illustrate the severity of this error source through flight and compensation experiments, where regular identification in highly dynamic applications becomes crucial to improve navigation accuracy.

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