Topic
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.
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TL;DR: This paper surveys thoroughly the research efforts taken in visual-inertial navigation research and strives to provide a concise but complete review of the related work in the hope to accelerate the VINS research and beyond in the authors' society as a whole.
Abstract: As inertial and visual sensors are becoming ubiquitous, visual-inertial navigation systems (VINS) have prevailed in a wide range of applications from mobile augmented reality to aerial navigation to autonomous driving, in part because of the complementary sensing capabilities and the decreasing costs and size of the sensors. In this paper, we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work -- which is unfortunately missing in the literature while being greatly demanded by researchers and engineers -- in the hope to accelerate the VINS research and beyond in our society as a whole.
136 citations
01 May 2020
TL;DR: A new benchmark containing more than 40 hours of IMU sensor data from 100 human subjects with ground-truth 3D trajectories under natural human motions, and novel neural inertial navigation architectures, making significant improvements for challenging motion cases are presented.
Abstract: This paper sets a new foundation for data-driven inertial navigation research, where the task is the estimation of horizontal positions and heading direction of a moving subject from a sequence of IMU sensor measurements from a phone. In contrast to existing methods, our method can handle varying phone orientations and placements.More concretely, the paper presents 1) a new benchmark containing more than 40 hours of IMU sensor data from 100 human subjects with ground-truth 3D trajectories under natural human motions; 2) novel neural inertial navigation architectures, making significant improvements for challenging motion cases; and 3) qualitative and quantitative evaluations of the competing methods over three inertial navigation benchmarks. We share the code and data to promote further research. (http://ronin.cs.sfu.ca).
136 citations
10 Nov 2011
TL;DR: A novel heading estimation scheme using a quaternion-based extended Kalman filter (EKF) that estimates magnetic disturbances and corrects for them is proposed.
Abstract: This paper presents a waist-worn Pedestrian Dead Reckoning (PDR) System that requires minimal end-user calibration. The PDR system is based on an Inertial Measurement Unit (IMU) comprising of a tri-axial accelerometer, a tri-axial magnetometer and a tri-axial gyroscope. We propose a novel heading estimation scheme using a quaternion-based extended Kalman filter (EKF) that estimates magnetic disturbances and corrects for them. Accelerometer measurements are used to detect step events and to estimate step lengths. Experimental results show that a relative distance error of about 3% to 8% can be obtained using our methods.
136 citations
08 Jul 2001
TL;DR: A localization algorithm based on Kalman filtering that tries to fuse information coming from an inexpensive single GPS with inertial and, sometimes uncertain, map based data leads to a motion whose precision is only related to current information quality.
Abstract: The use of the Global Positioning System (GPS) in outdoor localization is a quite common solution in large environments where no other references are available and positioning requirements are not so pressing. Of course, fine motion without the use of an expensive differential device is not an easy task even now that available precision has been greatly improved as the military encoding has been removed. We present a localization algorithm based on Kalman filtering that tries to fuse information coming from an inexpensive single GPS with inertial and, sometimes uncertain, map based data. The algorithm is able to produce an estimated configuration for the robot that can be successfully fed back in a navigation system, leading to a motion whose precision is only related to current information quality. Some experiments show difficulties and possible solutions to this sensor fusion problem.
135 citations
TL;DR: In this paper, a systematic analysis of the observability of an inertial navigation system (INS) in ground alignment with Bar-Itzhack and Berman's error model is presented.
Abstract: A systematic analysis of the observability of an inertial navigation system (INS) in ground alignment with Bar-Itzhack and Berman's error model is presented. It is shown that the unobservable states are separately contained in two decoupled subspaces. The constraints on the selection of unobservable states are discussed. An estimation algorithm which is derived fully from the horizontal velocity outputs for computing the misalignment angles is provided. It reveals that the azimuth error can be entirely estimated from the estimates of leveling error and leveling error rate, without using gyro output signals explicitly. >
135 citations