Collar Line Segments for Fast Odometry Estimation from Velodyne Point Clouds

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F16%3APU121570" target="_blank" >RIV/00216305:26230/16:PU121570 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7487648/" target="_blank" >http://ieeexplore.ieee.org/document/7487648/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICRA.2016.7487648" target="_blank" >10.1109/ICRA.2016.7487648</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Collar Line Segments for Fast Odometry Estimation from Velodyne Point Clouds

Popis výsledku v původním jazyce

We present a novel way of odometry estimation from Velodyne LiDAR point cloud scans. The aim of our work is to overcome the most painful issues of Velodyne data - the sparsity and the quantity of data points - in an efficient way, enabling more precise registration. Alignment of the point clouds which yields the final odometry is based on random sampling of the clouds using Collar Line Segments. The closest line segment pairs are identified in two sets of line segments obtained from two consequent Velodyne scans. From each pair of correspondences,  a  transformation aligning the matched line segments into a 3D plane is estimated. By this, significant planes (ground, walls, ...) are preserved among aligned point clouds. Evaluation using the KITTI dataset shows that our method outperforms publicly available and commonly used state-of-the-art method GICP for point cloud registration in both accuracy and speed, especially in cases where the scene lacks significant landmarks or in typical urban elements. For such environments, the registration error of our method  is reduced by 75% compared to the original GICP error.

Název v anglickém jazyce

Collar Line Segments for Fast Odometry Estimation from Velodyne Point Clouds

Popis výsledku anglicky

We present a novel way of odometry estimation from Velodyne LiDAR point cloud scans. The aim of our work is to overcome the most painful issues of Velodyne data - the sparsity and the quantity of data points - in an efficient way, enabling more precise registration. Alignment of the point clouds which yields the final odometry is based on random sampling of the clouds using Collar Line Segments. The closest line segment pairs are identified in two sets of line segments obtained from two consequent Velodyne scans. From each pair of correspondences,  a  transformation aligning the matched line segments into a 3D plane is estimated. By this, significant planes (ground, walls, ...) are preserved among aligned point clouds. Evaluation using the KITTI dataset shows that our method outperforms publicly available and commonly used state-of-the-art method GICP for point cloud registration in both accuracy and speed, especially in cases where the scene lacks significant landmarks or in typical urban elements. For such environments, the registration error of our method  is reduced by 75% compared to the original GICP error.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of IEEE International Conference on Robotics and Automation

ISBN

978-1-4673-8025-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

4486-4491

Název nakladatele

IEEE Computer Society

Místo vydání

Stockholm

Místo konání akce

Stockholm

Datum konání akce

16. 5. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000389516203125