Multisensor Online Transfer Learning for 3D LiDAR-based Human Detection with a Mobile Robot

Yan, Zhi and Sun, Li and Duckett, Tom and Bellotto, Nicola (2018) Multisensor Online Transfer Learning for 3D LiDAR-based Human Detection with a Mobile Robot. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1-5 Oct 2018, Madrid, Spain.

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Multisensor Online Transfer Learning for 3D LiDAR-based Human Detection with a Mobile Robot

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Abstract

Human detection and tracking is an essential task for service robots, where the combined use of multiple sensors has potential advantages that are yet to be fully exploited. In this paper, we introduce a framework allowing a robot to learn a new 3D LiDAR-based human classifier from other sensors over time, taking advantage of a multisensor tracking system. The main innovation is the use of different detectors for existing sensors (i.e. RGB-D camera, 2D LiDAR) to train, online, a new 3D LiDAR-based human classifier based on a new “trajectory probability”. Our framework uses this probability to check whether new detections belongs to a human trajectory, estimated by different sensors and/or detectors, and to learn a human classifier in a semi-supervised fashion. The framework has been implemented and tested on a real-world dataset collected by a mobile robot. We present experiments illustrating that our system is able to effectively learn from different sensors and from the environment, and that the performance of the 3D LiDAR-based human classification improves with the number of sensors/detectors used.

Keywords:online learning, mobile robotics, human detection
Subjects:H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:College of Science > School of Computer Science
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ID Code:32541
Deposited On:02 Jul 2018 14:23

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