Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models

TL;DR

This paper introduces a real-time incremental multimodal surface mapping method using Gaussian mixture models, achieving high-resolution environment reconstruction with efficient data compression and improved computational speed.

Contribution

It presents a novel spatial hash map for rapid GMM submap extraction and a data relevance approach, significantly enhancing speed and accuracy over existing methods.

Findings

Increased computational speed by an order of magnitude.

Better tradeoff between map accuracy and size.

Validated on simulated and real-world data.

Abstract

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.