複数センサのオンライン転移学習による移動ロボットの3D LiDAR人物検出
Multisensor Online Transfer Learning for 3D LiDAR-based Human Detection with a Mobile Robot
RGB-Dカメラや2D LiDARの検出結果と軌跡確率を活用し、3D LiDARベースの人物分類器をオンラインで半教師あり学習するフレームワークを提案・実機検証した。
著者: Zhi Yan, Li Sun, Tom Duckett, Nicola Bellotto
分類: cs.RO
原文アブストラクト
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 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, exploiting a so-called 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.
