2021/09/12 by Martin Wudenka, Wudenka, Martin, Marcus G. Müller +13 · 1 citation
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Advanced Vision and Imaging #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #FOS: Computer and information sciences #Mars Exploration Program #Mobile robot #Monocular #Odometry #Robot #Robotics (cs.RO) #Robotics and Sensor-Based Localization #Robustness (evolution) #Visual odometry #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.2109.05509
published in arXiv (Cornell University) (Cornell University) · Accepted to IROS 2021. Updated version corresponding to IROS camera-ready. The source code is publicly available at: https://github.com/DLR-RM/granite
arxiv created 2021/09/12 · openalex publication_date 2021/09/12 · arxiv updated 2021/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the future, extraterrestrial expeditions will not only be conducted by rovers but also by flying robots. The technical demonstration drone Ingenuity, that just landed on Mars, will mark the beginning of a new era of exploration unhindered by terrain traversability. Robust self-localization is crucial for that. Cameras that are lightweight, cheap and information-rich sensors are already used to estimate the ego-motion of vehicles. However, methods proven to work in man-made environments cannot simply be deployed on other planets. The highly repetitive textures present in the wastelands of Mars pose a huge challenge to descriptor matching based approaches. In this paper, we present an advanced robust monocular odometry algorithm that uses efficient optical flow tracking to obtain feature correspondences between images and a refined keyframe selection criterion. In contrast to most other approaches, our framework can also handle rotation-only motions that are particularly challenging for monocular odometry systems. Furthermore, we present a novel approach to estimate the current risk of scale drift based on a principal component analysis of the relative translation information matrix. This way we obtain an implicit measure of uncertainty. We evaluate the validity of our approach on all sequences of a challenging real-world dataset captured in a Mars-like environment and show that it outperforms state-of-the-art approaches.