Abstract
We present the sliding-window monocular visual inertial odometry that is accurate and robust to outliers by employing a new observation model grounded on the pentafocal geometric constraints. The previous approaches are dependent on the unknown 3D coordinates of the features to estimate the ego-motion. However, the inaccurate 3D position of the features can lead to poor performance in motion estimation. To overcome these limitations, we utilize the pentafocal geometry relationship between five images as camera observation model, which makes it unnecessary to estimate the 3D position of the features. Furthermore, we apply the pentafocal constraints in the 1-point random sample consensus (RANSAC) algorithm to find incorrect feature correspondences. We demonstrate the effectiveness of the proposed algorithm in two types of experiments: the KITTI driving scene dataset and the EuRoC micro aerial vehicle (MAV) flying dataset, both qualitatively and quantitatively. It shows more accurate state estimation performance compared to the well-known stereo visual odometry algorithm and current state-of-the-art visual inertial odometry methods.
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Recommended by Associate Editor Huaping Liu under the direction of Editor Duk-Sun Shim. This work was supported by the Seoul National University Research Grant in 2015, the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning (2014M1A3A3A02034854), and the Technology Innovation Program (10067206) funded by the Ministry of Trade, industry & Energy (MI, Korea).
Pyojin Kim received the B.S. degree in Mechanical Engineering from Yonsei University in 2013. He is currently pursuing the M.S. and Ph.D. degrees in the Department of Mechanical and Aerospace Engineering at Seoul National University. His research interests include 3D computer vision, visual odometry, and visual SLAM.
Hyon Lim received the B.S. and M.S. degrees in Electronic and Electrical Engineering from Inha University in 2008 and 2010, and the Ph.D. degree in the Department of Mechanical and Aerospace Engineering from Seoul National University in 2015. His research interests include computer vision, real-time visual SLAM, and applications of unmanned aerial vehicles.
H. Jin Kim received the B.S. degree from Korea Advanced Institute of Technology (KAIST) in 1995, and the M.S. and Ph.D. degrees in Mechanical Engineering from University of California, Berkeley (UC Berkeley), in 1999 and 2001, respectively. From 2002 to 2004, she was a Postdoctoral Researcher in Electrical Engineering and Computer Science (EECS), UC Berkeley. In September 2004 she joined the Department of Mechanical and Aerospace Engineering at Seoul National University, Seoul, Korea, as an Assistant Professor where she is currently a Professor. Her research interests include intelligent control of robotic systems and motion planning.
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Kim, P., Lim, H. & Kim, H.J. Visual Inertial Odometry with Pentafocal Geometric Constraints. Int. J. Control Autom. Syst. 16, 1962–1970 (2018). https://doi.org/10.1007/s12555-017-0200-5
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DOI: https://doi.org/10.1007/s12555-017-0200-5