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

TL;DR

This paper introduces GPGM-SLAM, a robust SLAM system for unstructured planetary environments that uses Gaussian Process Gradient Maps to improve loop closure detection and localization accuracy in ambiguous terrains.

Contribution

The paper presents a novel SLAM approach leveraging Gaussian Process Gradient Maps for enhanced robustness in natural, unstructured environments, enabling better loop closure detection.

Findings

Outperforms state-of-the-art visual SLAM methods in challenging terrains.

Effective loop closure detection using GPGMaps in natural environments.

Robust localization demonstrated on datasets from volcanic, desert, and planetary terrains.

Abstract

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