Authors:
Charles Hamesse
1
;
2
;
Hiep Luong
1
and
Rob Haelterman
2
Affiliations:
1
Ghent University, imec - IPI - URC, Ghent, Belgium
;
2
Royal Military Academy, Department of Mathematics, XR Lab, Brussels, Belgium
Keyword(s):
Monocular Visual Odometry, Head Motion, Synthetic Data.
Abstract:
Monocular visual odometry is a core component of visual Simultaneous Localization and Mapping (SLAM). Nowadays, headsets with a forward-pointing camera abound for a wide range of use cases such as extreme sports, firefighting or military interventions. Many of these headsets do not feature additional sensors such as a stereo camera or an IMU, thus evaluating the accuracy and robustness of monocular odometry remains critical. In this paper, we develop a novel framework for procedural synthetic dataset generation and a dedicated motion model for headset-mounted cameras. With our method, we study the performance of the leading classes of monocular visual odometry algorithms, namely feature-based, direct and deep learning-based methods. Our experiments lead to the following conclusions: i) the performance deterioration on headset-mounted camera images is mostly caused by head rotations and not by translations caused by human walking style, ii) feature-based methods are more robust to fas
t head rotations compared to direct and deep learning-based methods, and iii) it is crucial to develop uncertainty metrics for deep learning-based odometry algorithms.
(More)