LuVo: Lunar Visual Odometry Using Homography-Based Image Feature Matching

Abstract

We present LuVo, an initialization-free stereo visual odometry (VO) method developed for the VIPER lunar rover. We provide a novel stereo registration method using LightGlue image feature matching in a warped, locally planar space that improves matching robustness to larger baseline stereo sequences and repetitive terrain that traditionally challenge odometry approaches. We additionally introduce methods that increase the usable image region for matching by estimating a horizon cutoff in image space and enhance robustness to stereo correspondence failures using a Manhattan distance search for valid stereo points during cloud alignment. We evaluate the performance of LuVo on a dataset of 155 simulated lunar stereo sequences and show that it significantly improves registration accuracy and success rates for clouds separated by both expected driving ranges below eight meters and longer distance translations of up to 16 meters. While LuVo is developed for VIPER, it can be used in other environments featuring slip-prone and repetitive terrain that limit rover travel.