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.

翻译：本文提出一种增量式多模态表面建图方法，将环境表示为连续概率模型。该模型能够在实现高分辨率重建的同时，有效压缩空间与强度点云数据。本工作采用高斯混合模型（GMM）对环境进行表征。尽管现有基于GMM的建图研究已发展了利用信息论技术确定混合分量数量的方法，但这类方法要么仅能处理单次传感器观测数据而无法适用于增量式建图场景，要么在实时性方面存在不足（尤其对于需要高保真建模的应用）。为弥补上述不足，本文引入空间哈希图实现快速GMM子图提取，并提出点云中相关与冗余数据的甄别方法。相较于现有最先进的增量式GMM建图方法，这些创新将计算速度提升了一个数量级。此外，与当前最先进的建图方法（包括基于GMM及非GMM方法）相比，本方法在精度与规模之间实现了更优权衡。通过仿真数据与真实数据进行了系统评估，并开源相关软件以惠及机器人领域研究群体。