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arXiv · 1609.04705

Improving the Accuracy of Stereo Visual Odometry Using Visual Illumination Estimation

Lee Clement·Valentin Peretroukhin·Jonathan Kelly
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Abstract

In the absence of reliable and accurate GPS, visual odometry (VO) has emerged as an effective means of estimating the egomotion of robotic vehicles. Like any dead-reckoning technique, VO suffers from unbounded accumulation of drift error over time, but this accumulation can be limited by incorporating absolute orientation information from, for example, a sun sensor. In this paper, we leverage recent work on visual outdoor illumination estimation to show that estimation error in a stereo VO pipeline can be reduced by inferring the sun position from the same image stream used to compute VO, thereby gaining the benefits of sun sensing without requiring a dedicated sun sensor or the sun to be visible to the camera. We compare sun estimation methods based on hand-crafted visual cues and Convolutional Neural Networks (CNNs) and demonstrate our approach on a combined 7.8 km of urban driving from the popular KITTI dataset, achieving up to a 43% reduction in translational average root mean squared error (ARMSE) and a 59% reduction in final translational drift error compared to pure VO alone.

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Lee Clement, Valentin Peretroukhin, Jonathan Kelly. 2019-08-08. Improving the Accuracy of Stereo Visual Odometry Using Visual Illumination Estimation. https://doi.org/10.1007/978-3-319-50115-4_36

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