TY - JOUR
T1 - Observability Analysis and Performance Evaluation of EKF-Based Visual-Inertial Odometry with Online Intrinsic Camera Parameter Calibration
AU - Tsao, Shu Hua
AU - Jan, Shau Shiun
N1 - Funding Information:
Manuscript received November 15, 2018; revised December 10, 2018; accepted December 10, 2018. Date of publication December 14, 2018; date of current version March 7, 2019. This work was supported by the Ministry of Science and Technology, Taiwan, under Grant 107-2221-E-006-121. The associate editor coordinating the review of this paper and approving it for publication was Dr. Rosario Morello. (Corresponding author: Shau-Shiun Jan.) The authors are with the Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 70101, Taiwan (e-mail: P48051098@ gs.ncku.edu.tw; [email protected]). Digital Object Identifier 10.1109/JSEN.2018.2886764
Publisher Copyright:
© 2001-2012 IEEE.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - In this paper, we focus on the problem of online intrinsic camera parameter calibration for a visual-inertial system. Imprecise intrinsic camera parameters will result in unreliable pose estimation or even cause estimator divergence. Specifically, we present a nonlinear observability analysis of the system and prove that there are four unobservable directions spanning the right nullspace of the observability matrix, i.e., the rotation about the gravity vector and the positions in the global frame. We propose an extended Kalman filter-based visual-inertial odometry method for calibrating intrinsic camera parameters while estimating the pose simultaneously. The observability properties and the performance of the estimator are validated using both the simulated and real-world datasets.
AB - In this paper, we focus on the problem of online intrinsic camera parameter calibration for a visual-inertial system. Imprecise intrinsic camera parameters will result in unreliable pose estimation or even cause estimator divergence. Specifically, we present a nonlinear observability analysis of the system and prove that there are four unobservable directions spanning the right nullspace of the observability matrix, i.e., the rotation about the gravity vector and the positions in the global frame. We propose an extended Kalman filter-based visual-inertial odometry method for calibrating intrinsic camera parameters while estimating the pose simultaneously. The observability properties and the performance of the estimator are validated using both the simulated and real-world datasets.
UR - http://www.scopus.com/inward/record.url?scp=85058887176&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058887176&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2018.2886764
DO - 10.1109/JSEN.2018.2886764
M3 - Article
AN - SCOPUS:85058887176
SN - 1530-437X
VL - 19
SP - 2695
EP - 2703
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 7
M1 - 8576618
ER -