A sliding-window visual-IMU odometer based on tri-focal tensor geometry

Jwu-Sheng Hu, Ming Yuan Chen

Research output: Contribution to journalConference articlepeer-review

43 Scopus citations

Abstract

This paper presents an odometer architecture which combines a monocular camera and an inertial measurement unit (IMU). The trifocal tensor geometry relationship between three images is used as camera measurement information, which makes the proposed method without estimating the 3D position of feature point. In other words, the proposed method does not have to reconstruct environment. Meanwhile, the camera pose corresponding to each of the three images are refined in filter to form a multi-state constraint Kalman filter (MSCKF). Consequently, this paper proposes a sliding window odometry which has a balance between computational cost and accuracy. Compared with traditional visual odometry or simultaneous localization and mapping (SLAM) method, the proposed method not only meets the requirement of odometer in the ego-motion estimation, but also suit for real-time application. This paper further proposes a random sample consensus (RANSAC) algorithm which is based on three views geometry. The RANSAC algorithm can effectively reject feature points which are mismatch or located on independently moving objects, thus it make the overall algorithm capable of operating in dynamic environment. Experiments are conducted to show the effectiveness of the proposed method in real environment.

Original languageEnglish
Article number6907434
Pages (from-to)3963-3968
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
DOIs
StatePublished - 22 Sep 2014
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: 31 May 20147 Jun 2014

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