Mars in Short: Past and Present Geology and Climate

Space and society, Год журнала: 2024, Номер unknown, С. 5 - 50

Опубликована: Янв. 1, 2024

Язык: Английский

Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models

arXiv (Cornell University), Год журнала: 2023, Номер unknown

Опубликована: Янв. 1, 2023

This letter describes an incremental multimodal surface mapping methodology, which represents the environment as a continuous probabilistic model. 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 environment. While prior GMM-based works have developed methodologies determine number of components using information-theoretic techniques, these approaches either operate on individual sensor observations, making them unsuitable for mapping, or are not real-time viable, especially applications where high-fidelity modeling is required. To bridge gap, introduces hash map rapid GMM submap extraction combined with approach relevant redundant data cloud. These contributions increase computational speed by order magnitude compared state-of-the-art mapping. In addition, proposed yields superior tradeoff accuracy size when (both GMM- GMM-based). Evaluations conducted both simulated real-world software released open-source benefit robotics community.

Язык: Английский

Autumn 2022

Geoscientist, Год журнала: 2022, Номер 32(3)

Опубликована: Сен. 1, 2022