2018-10-23-ushani18a.md

title	abstract	keywords	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Feature Learning for Scene Flow Estimation from LIDAR	To perform tasks in dynamic environments, many mobile robots must estimate the motion in the surrounding world. Recently, techniques have been developed to estimate scene flow directly from LIDAR scans, relying on hand-designed features. In this work, we build an encoding network to learn features from an occupancy grid. The network is trained so that these features are discriminative in finding matching or non-matching locations between successive timesteps. This learned feature space is then leveraged to estimate scene flow. We evaluate our method on the KITTI dataset and demonstrate performance that improves upon the accuracy of the current state-of-the-art. We provide an implementation of our method at https://github.com/aushani/flsf.	feature learning, scene flow, LIDAR	inproceedings	Proceedings of Machine Learning Research	ushani18a	0	Feature Learning for Scene Flow Estimation from LIDAR	283	292	283-292	283	false	Ushani, Arash K. and Eustice, Ryan M.	given family Arash K. Ushani given family Ryan M. Eustice	2018-10-23		PMLR	Proceedings of The 2nd Conference on Robot Learning	87	inproceedings	date-parts 2018 10 23	http://proceedings.mlr.press/v87/ushani18a/ushani18a.pdf	label link Source code https://github.com/aushani/flsf