Horizontal Multi-Surface Random Sample Consensus for Robust Customizable Shelf Perception
Y.-T. Peng, J. Huang, and M. Cakmak, “Horizontal Multi-Surface Random Sample Consensus for Robust Customizable Shelf Perception,” Nov. 2019.
Abstract
Many robot tasks across domains from ware- houses to homes, such as object fetching, reconfiguration, and stowing, involve interactions with shelves. In this paper, we present a variant of the Random Sample Consensus (RANSAC) algorithm, with a sampling strategy that exploits the regular structure of shelves to robustly detect them. We examined the efficacy and usability of our algorithm by comparing it to a state-of-the-art surface segmentation algorithm implemented in PCL both with general parameters and with shelf-specific customized parameters. The results indicate that our algorithm has better precision and much higher recall in both settings.
BibTeX Entry
@inproceedings{peng2019irosw, title = {Horizontal Multi-Surface Random Sample Consensus for Robust Customizable Shelf Perception}, author = {Peng, Yu-Tang and Huang, Justin and Cakmak, Maya}, year = {2019}, month = nov, booktitle = {IROS 2019 Workshop on Different Approaches, the Same Goal: Autonomous Object Manipulation}, type = {workshop} }