Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions

TL;DR

This paper introduces a robust monocular visual odometry system tailored for flying robots on planetary missions, capable of handling challenging Martian terrains and rotation-only movements, with an innovative uncertainty estimation method.

Contribution

It presents a novel odometry algorithm using optical flow and keyframe selection, plus a new risk estimation approach for scale drift in Mars-like environments.

Findings

Outperforms state-of-the-art methods on Mars-like datasets

Handles rotation-only motions effectively

Provides implicit uncertainty measure for scale drift

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.