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.

中文翻译：

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基于3D LiDAR的人体检测的在线学习：点云聚类和分类方法的实验分析

本文提出了一种移动服务机器人使用3D LiDAR传感器在线学习分类器的系统，并在大型室内公共空间中进行了实验评估。学习框架需要最少的一组标记样本（例如一个或几个样本）来初始化分类器。然后在机器人的操作过程中对分类器进行迭代训练。使用多目标跟踪和一对“专家”来自动估计新的训练样本，以估计假阴性和假阳性。分类和跟踪都利用高效的实时聚类算法对3D点云数据进行分割。我们还引入了一项新功能，可以改善稀疏，远程点云中的人类分类。我们使用在大型室内公共空间中移动的人员的3D LiDAR数据集对我们的框架进行了广泛的评估，研究人员可以使用该数据集。实验表明，与最新技术相比，系统组件的影响和改进的人类分类。