Evaluating the impact of head motion on monocular visual odometry with synthetic data

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 fast 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