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This letter describes an incremental multimodal surface mapping methodology,which represents the environment as a continuous probabilistic model. Thismodel enables high-resolution reconstruction while simultaneously compressingspatial and intensity point cloud data. The strategy employed in this workutilizes Gaussian mixture models (GMMs) to represent the environment. Whileprior GMM-based mapping works have developed methodologies to determine thenumber of mixture components using information-theoretic techniques, theseapproaches either operate on individual sensor observations, making themunsuitable for incremental mapping, or are not real-time viable, especially forapplications where high-fidelity modeling is required. To bridge this gap, thisletter introduces a spatial hash map for rapid GMM submap extraction combinedwith an approach to determine relevant and redundant data in a point cloud.These contributions increase computational speed by an order of magnitudecompared to state-of-the-art incremental GMM-based mapping. In addition, theproposed approach yields a superior tradeoff in map accuracy and size whencompared to state-of-the-art mapping methodologies (both GMM- and notGMM-based). Evaluations are conducted using both simulated and real-world data.The software is released open-source to benefit the robotics community.

Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models