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Efficient Velodyne SLAM with point and plane features

Efficient Velodyne SLAM with point and plane features This paper develops and tests a plane based simultaneous localization and mapping algorithm capable of processing the uneven sampling density of Velodyne-style scanning LiDAR sensors in real-time. The algorithm uses an efficient plane detector to rapidly provide stable features, both for localization and as landmarks in a graph-based SLAM. When planes cannot be detected or when they provide insufficient support for localization, a novel constraint tracking algorithm selects a minimal set of supplemental point features to be provided to the localization solver. Several difficult indoor and outdoor datasets, totaling 6981 scans, each with $$\sim $$ ∼  70,000 points, are used to analyze the performance of the algorithm without the aid of any additional sensors. The results are compared to two competing state-of-the-art algorithms, GICP and LOAM, showing up to an order of magnitude faster runtime and superior accuracy on all datasets, with loop closure errors of 0.14–0.95 m, compared to 0.44–66.11 m. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autonomous Robots Springer Journals

Efficient Velodyne SLAM with point and plane features

Autonomous Robots , Volume 43 (5) – Aug 9, 2018

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References (42)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Engineering; Robotics and Automation; Artificial Intelligence; Computer Imaging, Vision, Pattern Recognition and Graphics; Control, Robotics, Mechatronics
ISSN
0929-5593
eISSN
1573-7527
DOI
10.1007/s10514-018-9794-6
Publisher site
See Article on Publisher Site

Abstract

This paper develops and tests a plane based simultaneous localization and mapping algorithm capable of processing the uneven sampling density of Velodyne-style scanning LiDAR sensors in real-time. The algorithm uses an efficient plane detector to rapidly provide stable features, both for localization and as landmarks in a graph-based SLAM. When planes cannot be detected or when they provide insufficient support for localization, a novel constraint tracking algorithm selects a minimal set of supplemental point features to be provided to the localization solver. Several difficult indoor and outdoor datasets, totaling 6981 scans, each with $$\sim $$ ∼  70,000 points, are used to analyze the performance of the algorithm without the aid of any additional sensors. The results are compared to two competing state-of-the-art algorithms, GICP and LOAM, showing up to an order of magnitude faster runtime and superior accuracy on all datasets, with loop closure errors of 0.14–0.95 m, compared to 0.44–66.11 m.

Journal

Autonomous RobotsSpringer Journals

Published: Aug 9, 2018

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