title: Tightly-coupled stereo visual-inertial navigation using point and line features.
abstract: This paper presents a novel approach for estimating the ego-motion of a vehicle in dynamic and unknown environments using tightly-coupled inertial and visual sensors. To improve the accuracy and robustness, we exploit the combination of point and line features to aid navigation. The mathematical framework is based on trifocal geometry among image triplets, which is simple and unified for point and line features. For the fusion algorithm design, we employ the Extended Kalman Filter (EKF) for error state prediction and covariance propagation, and the Sigma Point Kalman Filter (SPKF) for robust measurement updating in the presence of high nonlinearities. The outdoor and indoor experiments show that the combination of point and line features improves the estimation accuracy and robustness compared to the algorithm using point features alone.
authors.affiliation: College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China. kongxianglong51@gmail.com.
authors.forename: Xianglong
authors.initials: X
authors.lastname: Kong
authors.affiliation: College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China. wenqiwu_lit@hotmail.com.
authors.forename: Wenqi
authors.initials: W
authors.lastname: Wu
authors.affiliation: College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China. lilian-zhang@hotmail.com.
authors.forename: Lilian
authors.initials: L
authors.lastname: Zhang
authors.affiliation: College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China. yjwang@nudt.edu.cn.
authors.forename: Yujie
authors.initials: Y
authors.lastname: Wang
keywords: point and line features; tightly-coupled; trifocal geometry; vision-aided inertial navigation