  This letter describes an incremental multimodal surface mapping methodology,
which represents the environment as a continuous probabilistic model. This
model enables high-resolution reconstruction while simultaneously compressing
spatial and intensity point cloud data. The strategy employed in this work
utilizes Gaussian mixture models (GMMs) to represent the environment. While
prior GMM-based mapping works have developed methodologies to determine the
number of mixture components using information-theoretic techniques, these
approaches either operate on individual sensor observations, making them
unsuitable for incremental mapping, or are not real-time viable, especially for
applications where high-fidelity modeling is required. To bridge this gap, this
letter introduces a spatial hash map for rapid GMM submap extraction combined
with an approach to determine relevant and redundant data in a point cloud.
These contributions increase computational speed by an order of magnitude
compared to state-of-the-art incremental GMM-based mapping. In addition, the
proposed approach yields a superior tradeoff in map accuracy and size when
compared to state-of-the-art mapping methodologies (both GMM- and not
GMM-based). Evaluations are conducted using both simulated and real-world data.
The software is released open-source to benefit the robotics community.
