MBA-VO: Motion Blur Aware Visual Odometry

• URL: http://arxiv.org/abs/2103.13684v1
• Date: Thu, 25 Mar 2021 09:02:56 GMT
• Title: MBA-VO: Motion Blur Aware Visual Odometry
• Authors: Peidong Liu, Xingxing Zuo, Viktor Larsson and Marc Pollefeys
• Abstract summary: Motion blur is one of the major challenges remaining for visual odometry methods. In low-light conditions where longer exposure times are necessary, motion blur can appear even for relatively slow camera motions. We present a novel hybrid visual odometry pipeline with direct approach that explicitly models and estimates the camera's local trajectory within the exposure time.
• Score: 99.56896875807635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motion blur is one of the major challenges remaining for visual odometry methods. In low-light conditions where longer exposure times are necessary, motion blur can appear even for relatively slow camera motions. In this paper we present a novel hybrid visual odometry pipeline with direct approach that explicitly models and estimates the camera's local trajectory within the exposure time. This allows us to actively compensate for any motion blur that occurs due to the camera motion. In addition, we also contribute a novel benchmarking dataset for motion blur aware visual odometry. In experiments we show that by directly modeling the image formation process, we are able to improve robustness of the visual odometry, while keeping comparable accuracy as that for images without motion blur.

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