Probabilistic Depth Map Model for Rotation-Only Camera Motion in Semi-Dense Monocular SLAM

Handing rotation-only camera motion is challenge work for current direct (featureless) monocular SLAM with six degrees of freedom. In rotation-only camera motion, existing systems can't estimate and update depth map and will get tracking failure. In this paper, a probabilistic depth map model i...

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Bibliographic Details
Published in2016 International Conference on Virtual Reality and Visualization (ICVRV) pp. 8 - 15
Main Authors Yao Zhou, Feihu Yan, Zhong Zhou
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Bayes methods
Cameras
Computational modeling
Estimation
mapping
Probabilistic logic
rotation-only camera motion
semi-dense SLAM
Simultaneous localization and mapping
Tracking
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Summary:Handing rotation-only camera motion is challenge work for current direct (featureless) monocular SLAM with six degrees of freedom. In rotation-only camera motion, existing systems can't estimate and update depth map and will get tracking failure. In this paper, a probabilistic depth map model is proposed based on Bayesian estimation in a per-pixel level. Both rotation-only and general camera motion can be tracked and mapped with this depth map model in real-time. The experiment results illustrate that our approach can handles both general and rotation-only camera motion and creates a larger and denser maps compared to the state-of-art semi-dense SLAM system.
DOI:10.1109/ICVRV.2016.11