Visual Odometry by Multi-frame Feature Integration

Hernan Badino, Akihiro Yamamoto, and Takeo Kanade
International Workshop on Computer Vision for Autonomous Driving @ ICCV, December, 2013.

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This paper presents a novel stereo-based visual odometry approach that provides state-of-the-art results in real time, both indoors and outdoors. Our proposed method follows the procedure of computing optical flow and stereo disparity to minimize the re-projection error of tracked feature points. However, instead of following the traditional approach of performing this task using only consecutive frames, we propose a novel and computationally inexpensive technique that uses the whole history of the tracked feature points to compute the motion of the camera. In our technique, which we call multi-frame feature integration, the features measured and tracked over all past frames are integrated into a single, improved estimate. An augmented feature set, composed of the improved estimates, is added to the optimization algorithm, improving the accuracy of the computed motion and reducing ego-motion drift. Experimental results show that the proposed approach reduces pose error by up to 65% with a negligible additional computational cost of 3.8%. Furthermore, our algorithm outperforms all other known methods on the KITTI Vision Benchmark data set.

visual odometry, stereo, feature, keypoint, disparity, multi-frame

Number of pages: 8

Text Reference
Hernan Badino, Akihiro Yamamoto, and Takeo Kanade, "Visual Odometry by Multi-frame Feature Integration," International Workshop on Computer Vision for Autonomous Driving @ ICCV, December, 2013.

BibTeX Reference
   author = "Hernan Badino and Akihiro Yamamoto and Takeo Kanade",
   title = "Visual Odometry by Multi-frame Feature Integration",
   booktitle = "International Workshop on Computer Vision for Autonomous Driving @ ICCV",
   month = "December",
   year = "2013",