Adaptive Data Compression for Robot Perception
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 2746-2750.
Abstract
This paper concerns the creation of an efficient, continuous, non-parametric representation of surfaces implicit in 3D laser data as typically recorded by mobile robots. Our approach explicitly leverages the probabilistic nature of Gaussian Process regression to provide for a principled, adaptive subsampling which automatically prunes redundant data. The algorithm places no restriction on the complexity of the underlying surfaces and enables predictions at arbitrary locations and densities. We present results using real and synthetic data and show that our approach attains decimation factors in excess of two orders of magnitude without significant degradation in fidelity of the workspace reconstructions.
@inproceedings{ijcai2011p457,
title = {Adaptive Data Compression for Robot Perception},
author = {Smith, Mike and Posner, Ingmar and Newman, Paul M.},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {2746--2750},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-457},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-457},
}
TY - CPAPER TI - Adaptive Data Compression for Robot Perception AU - Smith, Mike AU - Posner, Ingmar AU - Newman, Paul M. T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 2746 EP - 2750 DO - 10.5591/978-1-57735-516-8/IJCAI11-457 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-457 ER -Copied
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