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Remote Sens. 2019, 11(1), 67;

Development of Stereo Visual Odometry Based on Photogrammetric Feature Optimization

Department of Geoinformatic Engineering, Inha University, 100 Inharo, Michuhol-gu, Incheon 22212, Korea
Author to whom correspondence should be addressed.
Received: 31 October 2018 / Revised: 20 December 2018 / Accepted: 26 December 2018 / Published: 1 January 2019
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One of the important image processing technologies is visual odometry (VO) technology. VO estimates platform motion through a sequence of images. VO is of interest in the virtual reality (VR) industry as well as the automobile industry because the construction cost is low. In this study, we developed stereo visual odometry (SVO) based on photogrammetric geometric interpretation. The proposed method performed feature optimization and pose estimation through photogrammetric bundle adjustment. After corresponding the point extraction step, the feature optimization was carried out with photogrammetry-based and vision-based optimization. Then, absolute orientation was performed for pose estimation through bundle adjustment. We used ten sequences provided by the Karlsruhe institute of technology and Toyota technological institute (KITTI) community. Through a two-step optimization process, we confirmed that the outliers, which were not removed by conventional outlier filters, were removed. We also were able to confirm the applicability of photogrammetric techniques to stereo visual odometry technology. View Full-Text
Keywords: stereo visual odometry; photogrammetry; feature optimization; pose estimation; KITTI stereo visual odometry; photogrammetry; feature optimization; pose estimation; KITTI

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Yoon, S.-J.; Kim, T. Development of Stereo Visual Odometry Based on Photogrammetric Feature Optimization. Remote Sens. 2019, 11, 67.

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