Recovering Stable Scale in Monocular SLAM Using Object-Supplemented Bundle Adjustment

Journal article

Without knowledge of the absolute baseline between images, the scale of a map from a single-camera simultaneous localization and mapping system is subject to calamitous drift over time. We describe a monocular approach that in addition to point measurements also considers object detections to resolve this scale ambiguity and drift. By placing an expectation on the size of the objects, the scale estimation can be seamlessly integrated into a bundle adjustment. When object observations are available, the local scale of the map is then determined jointly with the camera pose in local adjustments. Unlike many previous visual odometry methods, our approach does not impose restrictions such as constant camera height or planar roadways, and is therefore more widely applicable. We evaluate our approach on the KITTI data set and show that it reduces scale drift over long-range outdoor sequences with a total length of 40 km. As the scale of objects is known absolutely, metric accuracy is obtained for all sequences. Qualitative evaluation is also performed on video footage from a hand-held camera.

Authors: Frost, D; Prisacariu, V; Murray, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Journal: IEEE Transactions on Robotics
• Volume: 34
• Issue: 3
• Pages: 736-747
• Publication date: 2018-05-03
• Acceptance date: 2018-02-19
• DOI: 10.1109/TRO.2018.2820722
• EISSN: 1941-0468
• ISSN: 1552-3098
• Keywords: object recognition; monocular vision; Bundle adjustment; simultaneous localization and mapping (SLAM); scale drift