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Simultaneous Localization and Mapping (SLAM) techniques play a key roletowards long-term autonomy of mobile robots due to the ability to correctlocalization errors and produce consistent maps of an environment over time.Contrarily to urban or man-made environments, where the presence of uniqueobjects and structures offer unique cues for localization, the appearance ofunstructured natural environments is often ambiguous and self-similar,hindering the performances of loop closure detection. In this paper, we presentan approach to improve the robustness of place recognition in the context of asubmap-based stereo SLAM based on Gaussian Process Gradient Maps (GPGMaps).GPGMaps embed a continuous representation of the gradients of the local terrainelevation by means of Gaussian Process regression and Structured KernelInterpolation, given solely noisy elevation measurements. We leverage theimage-like structure of GPGMaps to detect loop closures using traditionalvisual features and Bag of Words. GPGMap matching is performed as an SE(2)alignment to establish loop closure constraints within a pose graph. Weevaluate the proposed pipeline on a variety of datasets recorded on Mt. Etna,Sicily and in the Morocco desert, respectively Moon- and Mars-likeenvironments, and we compare the localization performances withstate-of-the-art approaches for visual SLAM and visual loop closure detection.

GPGM-SLAM: a Robust SLAM System for Unstructured Planetary Environments with Gaussian Process Gradient Maps