Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models
This work addresses the problem of real-time, high-fidelity mapping for robotics, though it is incremental as it builds on existing GMM-based approaches.
The paper tackles incremental multimodal surface mapping by introducing a spatial hash map and data relevance determination for Gaussian mixture models, achieving an order of magnitude faster computational speed and a superior tradeoff in map accuracy and size compared to state-of-the-art methods.
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.