Abstract
In this paper, we present a robust framework suitable for conducting three-dimensional Simultaneous Localization and Mapping (3D SLAM) in a planetary worksite environment. By utilizing a laser rangefinder mounted on a rover platform, we have demonstrated an approach that is able to create globally consistent maps of natural, unstructured 3D terrain. The framework presented in this paper utilizes a sparse-feature-based approach, and conducts data association using a hybrid combination of feature constellations and dense data. To maintain global consistency, the measurements are resolved using a batch alignment algorithm, which is reinforced with batch outlier rejection to improve its robustness. Finally, a map is created from the alignment estimates and the dense data. Validation is provided using data gathered at two different planetary analogue facilities.
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