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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.


Papers
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Proceedings ArticleDOI
05 Mar 2005
TL;DR: An unscented Kalman filter is applied to the navigation problem, which leads to a consistent estimate of vehicle and feature states, and preliminary hardware test results showing navigation and mapping using an off-the-shelf inertial measurement unit and camera in a laboratory environment are presented.
Abstract: A method for passive GPS-free navigation of a small unmanned aerial vehicle with a minimal sensor suite (limited to an inertial measurement unit and a monocular camera) is presented. The navigation task is cast as a simultaneous localization and mapping (SLAM) problem. While SLAM has been the subject of a great deal of research, the highly non-linear system dynamics and limited sensor suite available in this application presents a unique set of challenges which have not previously been addressed. In this particular application solutions based on extended Kalman filters have been shown to diverge and alternate techniques are required. In this paper an unscented Kalman filter is applied to the navigation problem, which leads to a consistent estimate of vehicle and feature states. This paper presents: (a) simulation results showing mapping and navigation in three dimensions; and (b) preliminary hardware test results showing navigation and mapping using an off-the-shelf inertial measurement unit and camera in a laboratory environment

47 citations

Journal ArticleDOI
29 Sep 2011-Sensors
TL;DR: This paper presents a PDR solution that incorporates a drift correction method based on detecting the access ramps usually found in buildings that achieves Drift-free localization with positioning errors below 2 meters for 1,000-meter-long routes in a building with a few ramps.
Abstract: The localization of persons in indoor environments is nowadays an open problem. There are partial solutions based on the deployment of a network of sensors (Local Positioning Systems or LPS). Other solutions only require the installation of an inertial sensor on the person’s body (Pedestrian Dead-Reckoning or PDR). PDR solutions integrate the signals coming from an Inertial Measurement Unit (IMU), which usually contains 3 accelerometers and 3 gyroscopes. The main problem of PDR is the accumulation of positioning errors due to the drift caused by the noise in the sensors. This paper presents a PDR solution that incorporates a drift correction method based on detecting the access ramps usually found in buildings. The ramp correction method is implemented over a PDR framework that uses an Inertial Navigation algorithm (INS) and an IMU attached to the person’s foot. Unlike other approaches that use external sensors to correct the drift error, we only use one IMU on the foot. To detect a ramp, the slope of the terrain on which the user is walking, and the change in height sensed when moving forward, are estimated from the IMU. After detection, the ramp is checked for association with one of the existing in a database. For each associated ramp, a position correction is fed into the Kalman Filter in order to refine the INS-PDR solution. Drift-free localization is achieved with positioning errors below 2 meters for 1,000-meter-long routes in a building with a few ramps.

47 citations

Journal ArticleDOI
TL;DR: A novel IPN method based on shoe-mounted micro-electro-mechanical systems inertial measurement unit and ultra-wideband which is able to obtain high-precision position and orientation estimates at low cost and the system complexity is reduced.
Abstract: In the field of indoor pedestrian navigation (IPN), the orientation information of a pedestrian is often obtained by means of strap-down inertial navigation system (SINS). To deal with the problem of divergence in SINS based orientation estimates, additional orientation sensors, such as a camera, are needed to provide external orientation observations, resulting in increased cost and complexity of system. Although a low-cost magnetometer (or compass) can be used, it is significantly affected by geomagnetic disturbances indoors. Besides, the magnetometer can only give the heading observation which is insufficient to correct orientation errors in all three directions. In this paper, we propose a novel IPN method based on shoe-mounted micro-electro-mechanical systems inertial measurement unit and ultra-wideband. The biggest advantage of this method is able to obtain high-precision position and orientation estimates at low cost. In addition, in the proposed method, the data fusion is implemented by a quaternion Kalman filter which does not involve any complex linearization and hence the system complexity is reduced. Experimental results show that a decimeter level position accuracy is achieved and the orientation drifts can be limited to 0.066 radians in indoor environments.

47 citations

Proceedings ArticleDOI
09 May 2011
TL;DR: A navigation algorithm for full body state (position, velocity, and attitude) estimation that does not use any external reference (such as GPS, or visual landmarks) and is shown to work for two different dynamic turning gaits and on two terrains with significantly different friction.
Abstract: It is an important ability for any mobile robot to be able to estimate its posture and to gauge the distance it travelled. The information can be obtained from various sources. In this work, we have addressed this problem in a dynamic quadruped robot. We have designed and implemented a navigation algorithm for full body state (position, velocity, and attitude) estimation that does not use any external reference (such as GPS, or visual landmarks). Extended Kalman Filter was used to provide error estimation and data fusion from two independent sources of information: Inertial Navigation System mechanization algorithm processing raw inertial data, and legged odometry, which provided velocity aiding. We present a novel data-driven architecture for legged odometry that relies on a combination of joint sensor signals and pressure sensors. Our navigation system ensures precise tracking of a running robot's posture (roll and pitch), and satisfactory tracking of its position over medium time intervals. We have shown our method to work for two different dynamic turning gaits and on two terrains with significantly different friction. We have also successfully demonstrated how our method generalizes to different velocities.

47 citations

Journal ArticleDOI
TL;DR: The laboratory and field experiment results for a navigation-grade IMU prove that the proposed method can successfully identify the accelerometer nonlinear scale factor and improve the multi-position calibration accuracy.
Abstract: The calibration of an inertial measurement unit (IMU) is a key technique to improve the accuracy of an inertial navigation system. Adding more parameters into the model and reducing the estimation errors is essential for improving the calibration methods. Given its advantage of not requiring high-precision equipment, the multi-position calibration method has been widely discussed and has shown great potential in recent years. In this paper, the multi-position calibration method is improved by introducing the accelerometer nonlinear scale factor. The observation equations for the improved multi-position calibration method are established based on a nonlinear accelerometer model. The particle swarm optimization algorithm is adopted to solve the complicated nonlinear equations. In addition, Allan variance is used to determine the optimal data collection time. The accuracy and the robustness of the proposed calibration method are verified by the simulation test. The laboratory and field experiment results for a navigation-grade IMU prove that the proposed method can successfully identify the accelerometer nonlinear scale factor and improve the multi-position calibration accuracy. The comparison of several other calibration methods highlights the superior performance of the proposed method without precise orientation control.

47 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023309
2022657
2021491
2020889
20191,003
20181,013