  Future planetary missions will rely on rovers that can autonomously explore
and navigate in unstructured environments. An essential element is the ability
to recognize places that were already visited or mapped. In this work, we
leverage the ability of stereo cameras to provide both visual and depth
information, guiding the search and validation of loop closures from a
multi-modal perspective. We propose to augment submaps that are created by
aggregating stereo point clouds, with visual keyframes. Point clouds matches
are found by comparing CSHOT descriptors and validated by clustering, while
visual matches are established by comparing keyframes using Bag-of-Words (BoW)
and ORB descriptors. The relative transformations resulting from both keyframe
and point cloud matches are then fused to provide pose constraints between
submaps in our graph-based SLAM framework. Using the LRU rover, we performed
several tests in both an indoor laboratory environment as well as a challenging
planetary analog environment on Mount Etna, Italy. These environments consist
of areas where either keyframes or point clouds alone failed to provide
adequate matches demonstrating the benefit of the proposed multi-modal
approach.
