Evaluation of heterogeneous measurement outlier rejection schemes for robotic planetary surface mapping

In this paper, we describe the development and evaluation of a core algorithmic component for robust robotic planetary surface mapping. In particular, we consider the issue of outlier measurements when utilizing both odometry and sparse features for laser scan alignment. Due to the heterogeneity of the measurements and the relative scarcity of distinct geometric features in the planetary environment, we have found that the conventional outlier rejection methods in the current literature do not produce satisfactory classifications for accurate mapping performance. In light of these limitations, we develop a new approach capable of addressing these concerns. This includes a family of four outlier classification algorithms, which are incorporated through iterative reclassification into the batch alignment framework to provide robust surface mapping performance. Characterization of these outlier rejection schemes is presented using a combination of simulated data and real-world testing with an indoor rover.