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Rigid Scene Flow for 3D LiDAR Scans
Ayush Dewan and Tim Caselitz and Gian Diego Tipaldi, Wolfram Burgard
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016)
2016
dewan-16iros.pdf
Notes:
The perception of the dynamic aspects of the
environment is a highly relevant precondition for the realization
of autonomous robot system acting in the real world. In this
paper, we propose a novel method for estimating dense rigid
scene flow in 3D LiDAR scans. We formulate the problem
as an energy minimization problem, where we assume local
geometric constancy and incorporate regularization for smooth
motion fields. Analyzing the dynamics at point level helps in
inferring the fine-grained details of motion. We show results
on multiple sequences of the KITTI odometry dataset, where
we seamlessly estimate multiple motions pertaining to different
dynamic objects. Furthermore, we test our approach on a
dataset with pedestrians to show how our method adapts to a
case with non-rigid motion. For comparison we use the ground
truth from KITTI and show how our method outperforms
different ICP-based methods.
BibTeX:
@inproceedings{dewan2016iros,
author = {Ayush Dewan and Tim Caselitz and Gian Diego Tipaldi and Wolfram Burgard},
title = {Rigid Scene Flow for 3D LiDAR Scans},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016)},
year = 2016,
url = {http://ais.informatik.uni-freiburg.de/publications/papers/dewan-16iros.pdf},
address = {Daejeon, Korea}
}
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