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Autonomous Robots
Erschienen in: Autonomous Robots 2/2020

12.08.2019

Online learning for 3D LiDAR-based human detection: experimental analysis of point cloud clustering and classification methods

verfasst von: Zhi Yan, Tom Duckett, Nicola Bellotto

Erschienen in: Autonomous Robots | Ausgabe 2/2020

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Abstract

This paper presents a system for online learning of human classifiers by mobile service robots using 3D LiDAR sensors, and its experimental evaluation in a large indoor public space. The learning framework requires a minimal set of labelled samples (e.g. one or several samples) to initialise a classifier. The classifier is then retrained iteratively during operation of the robot. New training samples are generated automatically using multi-target tracking and a pair of “experts” to estimate false negatives and false positives. Both classification and tracking utilise an efficient real-time clustering algorithm for segmentation of 3D point cloud data. We also introduce a new feature to improve human classification in sparse, long-range point clouds. We provide an extensive evaluation of our the framework using a 3D LiDAR dataset of people moving in a large indoor public space, which is made available to the research community. The experiments demonstrate the influence of the system components and improved classification of humans compared to the state-of-the-art.
Vorheriger Artikel Attention-based active visual search for mobile robots
Nächster Artikel Simultaneous planning of sampling and optimization: study on lazy evaluation and configuration free space approximation for optimal motion planning algorithm

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Fußnoten
5
Due to privacy issues, currently panoramic images are not included in the dataset.
 
7
Although the “group” samples are not used in this paper, these annotations are included in our dataset to be used by other researchers for group tracking or other applications.
 
9
Dataset collection is also in progress at University of Lincoln, Czech Technical University in Prague, and University of Technology of Belfort-Montbéliard.
 
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Metadaten
Titel
Online learning for 3D LiDAR-based human detection: experimental analysis of point cloud clustering and classification methods
verfasst von
Zhi Yan
Tom Duckett
Nicola Bellotto
Publikationsdatum
12.08.2019
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 2/2020
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-019-09883-y

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