Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models

Kshitij Goel and Wennie Tabib

IEEE Robotics and Automation Letters (RA-L), Vol. 8 Issue 12, December 2023

teaser
(Left) Reconstruction obtained on a synthetic dataset. (Center) Precision, recall, and reconstruction error tradeoff with map size on disk for Octomap, Nvblox, fixed component GMMs, and the proposed approach. The total time taken for data association is also shown to be lower than a prior GMM-based approach. (Right) Reconstruction obtained on a real-world dataset. The proposed approach yields a map that requires less disk space than the competing methods while demonstrating at par or better reconstruction accuracy (i.e., low reconstruction error and high precision).

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.

Idea

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The incoming point cloud is first segmented into relevant and redundant data using the log-likelihood scores overt the existing model. The part of model that is updated is determined by the spatial hash map. After the model is updated, the spatial hash is updated using the sigma points of each mixture component. The use of spatial hash reduces the complexity of incremental mapping in the absence of ray casting.

Video

Presentation

Citation

@article{goel2023incremental,
	title={Incremental multimodal surface mapping via self-organizing gaussian mixture models},
	author={Goel, Kshitij and Tabib, Wennie},
	journal={IEEE Robotics and Automation Letters},
	volume={8},
	number={12},
	pages={8358--8365},
	year={2023},
	publisher={IEEE}
}