Direct line guidance odometry

Conference item

Modern visual odometry algorithms utilize sparse point-based features for tracking due to their low computational cost. Current state-of-the-art methods are split between indirect methods that process features extracted from the image, and indirect methods that deal directly on pixel intensities. In recent years, line-based features have been used in SLAM and have shown an increase in performance albeit with an increase in computational cost. In this paper, we propose an extension to a point-based direct monocular visual odometry method. Here we that uses lines to guide keypoint selection rather than acting as features. Points on a line are treated as stronger keypoints than those in other parts of the image, steering point-selection away from less distinctive points and thereby increasing efficiency. By combining intensity and geometry information from a set of points on a line, accuracy may also be increased.

Authors: Li, S-J; Ren, B; Liu, Y; Cheng, M-M; Frost, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Institute of Electrical and Electronics Engineers
• Host title: International Conference on Robotics and Automation (ICRA 2018)
• Journal: International Conference on Robotics and Automation (ICRA 2018)
• Publication date: 2018-09-13
• Acceptance date: 2018-01-15
• DOI: 10.1109/ICRA.2018.8461003
• ISSN: 1050-4729