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.

Linearized Error Propagation in Odometry

Abstract: The related fields of mobile robotics and ground vehicle localization lack a linearized theory of odometry error propagation. By contrast, the equivalent Schuler dynamics which apply to inertial guidance have been known and exploited for decades. In this paper, the general solution of linearized propagation dynamics of both systematic and random errors for vehicle odometry is developed and validated. The associated integral transforms are applied to the task of eliciting the major dynamic behaviors of errors for several forms of odometry. Interesting behaviors include path independence, response to symmetric inputs, zeros, extrema, monotonicity and conservation. Applications to systems theory, systems design, and calibration are illustrated. KEY WORDS—AUTHOR: PLEASE PROVIDE

GPS spoofing detection using RAIM with INS coupling

Abstract: In this work, we develop, implement, and test a monitor to detect GPS spoofing attacks using residual-based Receiver Autonomous Integrity Monitoring (RAIM) with inertial navigation sensors. Signal spoofing is a critical threat to all navigation applications that utilize GNSS, and is especially hazardous in aviation applications. This work develops a new method to directly detect spoofing using a GPS/INS integrated navigation system that incorporates fault detection concepts based on RAIM. The method is also capable of providing an upper bound on the proposed monitor's integrity risk.

Development and Evaluation of a UAV-Photogrammetry System for Precise 3D Environmental Modeling

Abstract: The specific requirements of UAV-photogrammetry necessitate particular solutions for system development, which have mostly been ignored or not assessed adequately in recent studies. Accordingly, this paper presents the methodological and experimental aspects of correctly implementing a UAV-photogrammetry system. The hardware of the system consists of an electric-powered helicopter, a high-resolution digital camera and an inertial navigation system. The software of the system includes the in-house programs specifically designed for camera calibration, platform calibration, system integration, on-board data acquisition, flight planning and on-the-job self-calibration. The detailed features of the system are discussed, and solutions are proposed in order to enhance the system and its photogrammetric outputs. The developed system is extensively tested for precise modeling of the challenging environment of an open-pit gravel mine. The accuracy of the results is evaluated under various mapping conditions, including direct georeferencing and indirect georeferencing with different numbers, distributions and types of ground control points. Additionally, the effects of imaging configuration and network stability on modeling accuracy are assessed. The experiments demonstrated that 1.55 m horizontal and 3.16 m vertical absolute modeling accuracy could be achieved via direct geo-referencing, which was improved to 0.4 cm and 1.7 cm after indirect geo-referencing.

Initial calibration of an inertial measurement unit using an optical position tracking system

Abstract: A reliable calibration procedure of a standard six degree-of-freedom inertial measurement unit (IMU) is presented. Mathematical models are derived for the three accelerometers and three rate gyros, taking into account the sensor axis misalignments, accelerometer offsets, electrical gains, and biases inherent in the manufacture of an IMU. The inertial sensors are calibrated using data from a 3D optical tracking system that measures the position coordinates of markers attached to the IMU. Inertial sensor signals and optical tracking data are obtained by manually moving the IMU. Using vector methods, the quaternion corresponding to the IMU platform orientation is obtained, along with its acceleration, velocity, and position. Given this kinematics information, the sensor models are used in a nonlinear least squares algorithm to solve for the unknown calibration parameters. The calibration procedure is verified through extensive experimentation.

Bridging GPS outages using neural network estimates of INS position and velocity errors

Abstract: INS and GPS are commonly integrated using a Kalman filter (KF) to provide a robust navigation solution, overcoming situations of GPS satellite signals blockage. This research presents an alternative method of bridging GPS outages requiring no prior knowledge of the INS and GPS sensor characteristics, called the artificial-intelligence-based segmented forward predictor. This method uses radial basis function neural networks to predict INS position and velocity errors during GPS outages, resulting in reliable performance. The performance of the proposed method is examined using real field test data of both navigational and tactical grade INS integrated with GPS. The results have shown that the proposed method outperforms KF, especially during long GPS outages.