Mobile robots require knowledge of the environment, especially of humans located in its vicinity. While the most common approaches for detecting humans involve computer vision, an often overlooked hardware feature of robots for people detection are their 2D range finders. These were originally intended for obstacle avoidance and mapping/SLAM tasks. In most robots, they are conveniently located at a height approximately between the ankle and the knee, so they can be used for detecting people too, and with a larger field of view and depth resolution compared to cameras. In this paper, we present a new dataset for people detection using knee-high 2D range finders called FROG. This dataset has greater laser resolution, scanning frequency, and more complete annotation data compared to existing datasets such as DROW. Particularly, the FROG dataset contains annotations for 100% of its laser scans (unlike DROW which only annotates 5%), 17x more annotated scans, 100x more people annotations, and over twice the distance traveled by the robot. We propose a benchmark based on the FROG dataset, and analyze a collection of state-of-the-art people detectors based on 2D range finder data. We also propose and evaluate a new end-to-end deep learning approach for people detection. Our solution works with the raw sensor data directly (not needing hand-crafted input data features), thus avoiding CPU preprocessing and releasing the developer of understanding specific domain heuristics. Experimental results show how the proposed people detector attains results comparable to the state of the art, while an optimized implementation for ROS can operate at more than 500 Hz.

翻译：移动机器人需要了解环境信息，尤其是其附近的人员位置。尽管最常见的人员检测方法涉及计算机视觉，但机器人用于人员检测的一个常被忽视的硬件特性是其二维测距仪。这些传感器最初设计用于避障和建图/SLAM任务。在大多数机器人中，它们通常安装在脚踝至膝盖之间的高度，因此也可用于人员检测，并且相比相机具有更大的视场和深度分辨率。本文提出了一种名为FROG的新型数据集，专为使用膝部高度二维测距仪进行人员检测而设计。与现有数据集（如DROW）相比，该数据集具有更高的激光分辨率、扫描频率以及更完整的标注数据。具体而言，FROG数据集对其100%的激光扫描进行了标注（而DROW仅标注5%），标注扫描数量多17倍，人员标注数量多100倍，机器人移动距离超过两倍。我们基于FROG数据集提出了基准测试方案，并分析了一系列基于二维测距仪数据的先进人员检测器。同时，我们提出并评估了一种新的端到端深度学习人员检测方法。我们的解决方案直接处理原始传感器数据（无需手工设计输入特征），从而避免了CPU预处理，并使开发者无需理解特定领域的启发式规则。实验结果表明，所提出的人员检测器取得了与先进技术相当的性能，而其针对ROS的优化实现可达到超过500 Hz的运行频率。