TY - JOUR
T1 - Observability Analysis and Consistency Improvements for Visual-Inertial Odometry on the Matrix Lie Group of Extended Poses
AU - Tsao, Shu Hua
AU - Jan, Shau Shiun
N1 - Funding Information:
Manuscript received November 17, 2020; revised December 16, 2020; accepted December 16, 2020. Date of publication December 23, 2020; date of current version February 17, 2021. This work was supported by the Ministry of Science and Technology, Taiwan, under Grant 108-2221-E-006-070. The associate editor coordinating the review of this article and approving it for publication was Prof. Kazuaki Sawada. (Corresponding author: Shau-Shiun Jan.) The authors are with the Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 70101, Taiwan (e-mail: [email protected]; [email protected]). Digital Object Identifier 10.1109/JSEN.2020.3046718
Publisher Copyright:
© 2001-2012 IEEE.
PY - 2021/3/15
Y1 - 2021/3/15
N2 - In this paper, we present a novel extended Kalman filter (EKF)-based visual-inertial odometry for robotic platforms by modeling the state space as the recently proposed matrix Lie group of extended poses. Specifically, we found that the proposed estimator suffers from an inconsistency similar to that of the conventional SO(3)× R6 uncertainty representation from the standpoint of an observability analysis. The inconsistency mainly is a result of spurious information along the unobservable directions. An inconsistent estimator would lead to overconfidently reducing the state uncertainty and larger estimation errors that would in turn cause system divergence. We applied the first-estimate Jacobian (FEJ) framework and observability constrained (OC) techniques to avoid spurious information and improve consistency. The performance of the proposed estimator is validated using both simulated and real-world datasets.
AB - In this paper, we present a novel extended Kalman filter (EKF)-based visual-inertial odometry for robotic platforms by modeling the state space as the recently proposed matrix Lie group of extended poses. Specifically, we found that the proposed estimator suffers from an inconsistency similar to that of the conventional SO(3)× R6 uncertainty representation from the standpoint of an observability analysis. The inconsistency mainly is a result of spurious information along the unobservable directions. An inconsistent estimator would lead to overconfidently reducing the state uncertainty and larger estimation errors that would in turn cause system divergence. We applied the first-estimate Jacobian (FEJ) framework and observability constrained (OC) techniques to avoid spurious information and improve consistency. The performance of the proposed estimator is validated using both simulated and real-world datasets.
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U2 - 10.1109/JSEN.2020.3046718
DO - 10.1109/JSEN.2020.3046718
M3 - Article
AN - SCOPUS:85098796440
SN - 1530-437X
VL - 21
SP - 8341
EP - 8353
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 6
M1 - 9305686
ER -