Carnegie Mellon University
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.

  • Adobe portable document format (pdf) (477KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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",