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“A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces” by M. Shienman, O. Levy-Or, M. Kaess, and V. Indelman. In Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS, (Abu Dhabi, UAE), Oct. 2024.
We introduce an innovative method for incremental nonparametric probabilistic inference in high-dimensional state spaces. Our approach leverages slices from high-dimensional surfaces to efficiently approximate posterior distributions of any shape. Unlike many existing graph-based methods, our slices perspective eliminates the need for additional intermediate reconstructions, maintaining a more accurate representation of posterior distributions. Additionally, we propose a novel heuristic to balance between accuracy and efficiency, enabling real-time operation in nonparametric scenarios. In empirical evaluations on synthetic and real-world datasets, our slices approach consistently outperforms other state-of-the-art methods. It demonstrates superior accuracy and achieves a significant reduction in computational complexity, often by an order of magnitude.
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BibTeX entry:
@inproceedings{Shienman24iros,
author = {M. Shienman and O. Levy-Or and M. Kaess and V. Indelman},
title = {A Slices Perspective for Incremental Nonparametric Inference
in High Dimensional State Spaces},
booktitle = {Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and
Systems, IROS},
address = {Abu Dhabi, UAE},
month = oct,
year = {2024}
}