Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions

• URL: http://arxiv.org/abs/2109.05509v1
• Date: Sun, 12 Sep 2021 12:52:20 GMT
• Title: Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions
• Authors: Martin Wudenka and Marcus G. M\"uller and Nikolaus Demmel and Armin Wedler and Rudolph Triebel and Daniel Cremers and Wolfgang St\"urzl
• Abstract summary: Ingenuity, that just landed on Mars, will mark the beginning of a new era of exploration unhindered by traversability. We present an advanced robust monocular odometry algorithm that uses efficient optical flow tracking. We also present a novel approach to estimate the current risk of scale drift based on a principal component analysis of the relative translation information matrix.
• Score: 49.79068659889639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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