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.

Real-Time Vision-Aided Localization and Navigation Based on Three-View Geometry

Abstract: A new method for vision-aided navigation based on three-view geometry is presented. The main goal of the proposed method is to provide position estimation in GPS-denied environments for vehicles equipped with a standard inertial navigation system (INS) and a single camera only, without using any a priori information. Images taken along the trajectory are stored and associated with partial navigation data. By using sets of three overlapping images and the concomitant navigation data, constraints relating the motion between the time instances of the three images are developed. These constraints include, in addition to the well-known epipolar constraints, a new constraint related to the three-view geometry of a general scene. The scale ambiguity, inherent to pure computer vision-based motion estimation techniques, is resolved by utilizing the navigation data attached to each image. The developed constraints are fused with an INS using an implicit extended Kalman filter. The new method reduces position errors in all axes to the levels present while the first two images were captured. Navigation errors in other parameters are also reduced, including velocity errors in all axes. Reduced computational resources are required compared with bundle adjustment and simultaneous localization and mapping (SLAM). The proposed method was experimentally validated using real navigation and imagery data. A statistical study based on simulated navigation and synthetic images is presented as well.

Time Synchronization Errors in Loosely Coupled GPS-Aided Inertial Navigation Systems

Abstract: The effects of data time synchronization errors in a loosely coupled Global-Positioning-System (GPS)-aided inertial navigation system (INS) are studied and quantified in terms of the increased mean square error (MSE) of the navigation solution. An expression for evaluating the MSE of the navigation solution, given the vehicle trajectory and the model of the INS error dynamics, is derived. Thereafter, a software-based time synchronization method, where the time synchronization error is included as a state to be estimated by the data integration filter, is proposed. A practical approach to the implementation of the proposed time synchronization method is also briefly described. Moreover, an expression for the MSE of the navigation solution in the system that employs the proposed synchronization method is derived. Finally, through simulations and tests with real-world data, the correctness of the derived MSE expressions is validated, and the application of the proposed synchronization method is shown. The test results show that, with the proposed synchronization approach, a data time synchronization, which is accurate to the order of a few milliseconds, can be achieved.

Evaluation of a solid-state gyroscope for robotics applications

Abstract: The evaluation of a low-cost solid-state gyroscope for robotics applications is described. An error model for the sensor is generated and included in a Kalman filter for estimating the orientation of a moving robot vehicle. Orientation estimation with the error model is compared to the performance when the error model is excluded from the system. The results demonstrate that without error compensation, the error in idealization is between 5-15/spl deg//min but can be improved at least by a factor of 5 if an adequate error model is supplied. Like all inertial systems, the platform requires additional information from some absolute position-sensing mechanism to overcome long-term drift. However, the results show that with careful and detailed modeling of error sources, inertial sensors can provide valuable orientation information for mobile robot applications. >

Evaluation of Matrix Square Root Operations for UKF within a UAV GPS/INS Sensor Fusion Application

Abstract: Using an Unscented Kalman Filter (UKF) as the nonlinear estimator within a Global Positioning System/Inertial Navigation System (GPS/INS) sensor fusion algorithm for attitude estimation, various methods of calculating the matrix square root were discussed and compared. Specifically, the diagonalization method, Schur method, Cholesky method, and five different iterative methods were compared. Additionally, a different method of handling the matrix square root requirement, the square-root UKF (SR-UKF), was evaluated. The different matrix square root calculations were compared based on computational requirements and the sensor fusion attitude estimation performance, which was evaluated using flight data from an Unmanned Aerial Vehicle (UAV). The roll and pitch angle estimates were compared with independently measured values from a high quality mechanical vertical gyroscope. This manuscript represents the first comprehensive analysis of the matrix square root calculations in the context of UKF. From this analysis, it was determined that the best overall matrix square root calculation for UKF applications in terms of performance and execution time is the Cholesky method.