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：本文提出了一种增量式多模态表面映射方法，将环境表示为连续概率模型。该模型在压缩空间与强度点云数据的同时，能够实现高分辨率重建。本研究采用高斯混合模型（GMM）对环境进行表征。尽管现有基于GMM的映射工作已开发出利用信息论技术确定混合分量数量的方法，但这些方法要么仅适用于单个传感器观测值（不适合增量式映射），要么无法满足实时性要求，尤其在高保真建模需求的场景中。为弥合这一差距，本文引入空间哈希映射实现快速GMM子图提取，并结合一种点云相关性与冗余性判定方法。这些创新使计算速度相比当前最先进的增量式GMM映射方法提升一个数量级。此外，与现有最先进的映射方法（包括GMM及非GMM方法）相比，本方法在映射精度与规模之间实现了更优的权衡。评估实验采用模拟数据与真实世界数据完成。相关软件已开源发布，以惠及机器人领域研究群体。