Inertial navigation system

About: Inertial navigation system is a research topic. Over the lifetime, 14582 publications have been published within this topic receiving 190618 citations. The topic is also known as: intertial guidance system & inertial reference platform.

System and method for terrain feature tracking

Abstract: System and method for tracking obstacles by an autonomous vehicle. Localization sensors (i.e., sensors to measure pitch, roll, and yaw, and systems including an inertial navigation system, a compass, a global positioning system, or an odometer) detect the position of the vehicle. Perception sensors (e.g., LIDAR, stereo vision, infrared vision, radar, or sonar) assess the environment about the vehicle. Using these sensors, locations of terrain features relative to the vehicle are computed and kept up-to-date. The vehicle trajectory is adjusted to avoid terrain features that are obstacles in the path of the vehicle.

Application of Kalman filtering to the calibration and alignment of inertial navigation systems

Abstract: Problem areas and practical solutions in the development of large-dimension Kalman filters for the calibration and alignment of complex inertial guidance systems are discussed. The basic vector attitude error equation is augmented by gyro and accelerometer unknown parameters. The parameter estimation problem is converted into a state estimation problem. A complete approach and description of the dual extended Kalman filter, one for accelerometers and one for gyros, is given. To reduce computational load, a technique of prefiltering (data compression or measurement averaging) has been implemented in the mechanization with very little degradation in the performance of the filter. The models of gyros and accelerometers used are described in detail. A technique for generating parameter excitation trajectories which provides observability of instrument parameters has been developed by maximizing the information matrix. A typical set of results for a simulator data set for parameter estimates and innovation sequences is given to show the performance, convergence, accuracy, and stability of the filter estimates. >

USBL/INS Tightly-Coupled Integration Technique for Underwater Vehicles

Abstract: This paper presents a new Ultra-Short Baseline (USBL) tightly-coupled integration technique to enhance error estimation in low-cost strap-down Inertial Navigation Systems (INSs) with application to underwater vehicles. In the proposed strategy the acoustic array spatial information is directly exploited resorting to the Extended Kalman Filter implemented in a direct feedback structure. The determination and stochastic characterization of the round trip travel time are obtained resorting to pulse detection matched filters of acoustic signals modulated using spread-spectrum Code Division Multiple Access (CDMA). The performance of the overall navigation system is assessed in simulation and compared with a conventional loosely-coupled solution that consists of solving separately the triangulation and sensor fusion problems. From the simulation results it can be concluded that the proposed technique enhances the position, orientation, and sensors biases estimates accuracy.

Visual localization and obstacle avoidance method and system for unmanned plane

Abstract: The invention provides a visual localization and obstacle avoidance method and a visual localization and obstacle avoidance system for an unmanned plane. The method comprises the following steps that an unmanned plane-mounted camera acquires visual sensing information of the unmanned plane, and acquires inertial navigation data by an inertia measuring unit of the unmanned plane; a remote control system receives visual sensing information and inertial navigation data, and obtains obstacle information in a flight environment where the unmanned plane is positioned according to the visual sensing information; an external camera performs visual localization on the unmanned plane to obtain the position information of the unmanned plane; the remote control system plans the flight path of the unmanned plane according to the obstacle information and the position information of the unmanned plane, and generates a flight control command according to the inertial navigation data and the flight path; and the unmanned plane receives the flight control command to control the unmanned plane to avoid obstacles. According to the embodiment of the invention, the problems of visual localization and obstacle avoidance of the unmanned plane are solved, and the unmanned plane has the capability of completing visual obstacle avoidance by using the plane-mounted camera and a positioning camera.

Integrated navigation system using camera and gimbaled laser scanner for indoor and outdoor autonomous flight of UAVs

Abstract: This paper describes an integrated navigation sensor module, including a camera, a laser scanner, and an inertial sensor, for unmanned aerial vehicles (UAVs) to fly both indoors and outdoors. The camera and the gimbaled laser sensor work in a complementary manner to extract feature points from the environment around the vehicle. The features are processed using an online extended Kalman filter (EKF) in simultaneous localization and mapping (SLAM) algorithm to estimate the navigational states of the vehicle. In this paper, a new method is proposed for calibrating a camera and a gimbaled laser sensor. This calibration method uses a simple visual marker to calibrate the camera and the laser scanner with each other. We also propose a real-time navigation algorithm based on the EKF SLAM algorithm, which is suitable for our camera-laser sensor package. The algorithm merges image features with laser range data for state estimation. Finally, these sensors and algorithms are implemented on our octo-rotor UAV platform and the result shows that our onboard navigation module can provide a real-time three-dimensional navigation solution without any assumptions or prior information on the surroundings.