ICRA 2012 Paper Abstract

Close

Paper TuB09.6

Li, Mingyang (University of California, Riverside), Mourikis, Anastasios (University of California, Riverside)

Improving the Accuracy of EKF-Based Visual-Inertial Odometry

Scheduled for presentation during the Regular Session "Localization" (TuB09), Tuesday, May 15, 2012, 11:45−12:00, Meeting Room 9 (Sa)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on August 21, 2018

Keywords Localization, Visual Navigation, Sensor Fusion

Abstract

In this paper, we perform a rigorous analysis of EKF-based visual-inertial odometry (VIO) and present a method for improving its performance. Specifically, we examine the properties of EKF-based VIO, and show that the standard way of computing Jacobians in the filter inevitably causes inconsistency and loss of accuracy. This result is derived based on an observability analysis of the EKF's linearized system model, which proves that the yaw erroneously appears to be observable. In order to address this problem, we propose modifications to the multi-state constraint Kalman filter (MSCKF) algorithm, which ensure the correct observability properties without incurring additional computational cost. Extensive simulation tests and real-world experiments demonstrate that the modified MSCKF algorithm outperforms competing methods, both in terms of consistency and accuracy.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2018 PaperCept, Inc.
Page generated 2018-08-21  18:11:27 PST  Terms of use