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 localizatio...

Full description

Saved in:
Bibliographic Details
Published in:Autonomous robots 2019-06, Vol.43 (5), p.1207-1224
Main Authors: Grant, W. Shane, Voorhies, Randolph C., Itti, Laurent
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computer Imaging
Control
Datasets
Engineering
Lidar
Localization
Mechatronics
Pattern Recognition and Graphics
Robotics
Robotics and Automation
Sensors
Simultaneous localization and mapping
Vision
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 ∼  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.
ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-018-9794-6