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Towards Robust Visual Odometry with a Multi-Camera System

2018

Conference Paper

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We present a visual odometry (VO) algorithm for a multi-camera system and robust operation in challenging environments. Our algorithm consists of a pose tracker and a local mapper. The tracker estimates the current pose by minimizing photometric errors between the most recent keyframe and the current frame. The mapper initializes the depths of all sampled feature points using plane-sweeping stereo. To reduce pose drift, a sliding window optimizer is used to refine poses and structure jointly. Our formulation is flexible enough to support an arbitrary number of stereo cameras. We evaluate our algorithm thoroughly on five datasets. The datasets were captured in different conditions: daytime, night-time with near-infrared (NIR) illumination and night-time without NIR illumination. Experimental results show that a multi-camera setup makes the VO more robust to challenging environments, especially night-time conditions, in which a single stereo configuration fails easily due to the lack of features.

Author(s): Peidong Liu and Marcel Geppert and Lionel Heng and Torsten Sattler and Andreas Geiger and Marc Pollefeys
Book Title: International Conference on Intelligent Robots and Systems (IROS) 2018
Year: 2018
Month: October

Department(s): Autonomous Vision
Bibtex Type: Conference Paper (inproceedings)

Event Name: International Conference on Intelligent Robots and Systems 2017
Event Place: Madrid, Spain

Links: pdf

BibTex

@inproceedings{Liu2018IROS,
  title = {Towards Robust Visual Odometry with a Multi-Camera System },
  author = {},
  booktitle = {International Conference on Intelligent Robots and Systems (IROS) 2018},
  month = oct,
  year = {2018},
  month_numeric = {10}
}