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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Notes
- 1.
Results from several state-of-the-art VO systems on the KITTI odometry benchmark can be found at http://www.cvlibs.net/datasets/kitti/eval_odometry.php.
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Clement, L., Peretroukhin, V., Kelly, J. (2017). Improving the Accuracy of Stereo Visual Odometry Using Visual Illumination Estimation. In: Kulić, D., Nakamura, Y., Khatib, O., Venture, G. (eds) 2016 International Symposium on Experimental Robotics. ISER 2016. Springer Proceedings in Advanced Robotics, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-50115-4_36
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