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

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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.

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Notes

  1. http://www.cvpapers.com/datasets.html.

  2. https://github.com/LCAS/online_learning.

  3. http://lcas.lincoln.ac.uk/wp/research/data-sets-software/l-cas-3d-point-cloud-people-dataset/.

  4. http://www.flobot.eu.

  5. Due to privacy issues, currently panoramic images are not included in the dataset.

  6. http://pointclouds.org/documentation/tutorials/pcd_file_format.php.

  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.

  8. The data is available at: https://github.com/LCAS/cloud_annotation_tool.

  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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Authors and Affiliations

  1. Distributed Artificial Intelligence and Knowledge Laboratory (CIAD), University of Technology of Belfort-Montbéliard (UTBM), Belfort, France

    Zhi Yan

  2. Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln, Lincoln, UK

    Tom Duckett & Nicola Bellotto

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  1. Zhi Yan
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  2. Tom Duckett
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  3. Nicola Bellotto
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Correspondence to Zhi Yan.

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant agreement No 645376 (FLOBOT).

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Yan, Z., Duckett, T. & Bellotto, N. Online learning for 3D LiDAR-based human detection: experimental analysis of point cloud clustering and classification methods. Auton Robot 44, 147–164 (2020). https://doi.org/10.1007/s10514-019-09883-y

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