School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China.
Sensors (Basel). 2021 Feb 1;21(3):961. doi: 10.3390/s21030961.
This paper describes the development of a laser-based people detection and obstacle avoidance algorithm for a differential-drive robot, which is used for transporting materials along a reference path in hospital domains. Detecting humans from laser data is an important functionality for the safety of navigation in the shared workspace with people. Nevertheless, traditional methods normally utilize machine learning techniques on hand-crafted geometrical features extracted from individual clusters. Moreover, the datasets used to train the models are usually small and need to manually label every laser scan, increasing the difficulty and cost of deploying people detection algorithms in new environments. To tackle these problems, (1) we propose a novel deep learning-based method, which uses the deep neural network in a sliding window fashion to effectively classify every single point of a laser scan. (2) To increase the speed of inference without losing performance, we use a jump distance clustering method to decrease the number of points needed to be evaluated. (3) To reduce the workload of labeling data, we also propose an approach to automatically annotate datasets collected in real scenarios. In general, the proposed approach runs in real-time and performs much better than traditional methods. Secondly, conventional pure reactive obstacle avoidance algorithms can produce inefficient and oscillatory behaviors in dynamic environments, making pedestrians confused and possibly leading to dangerous reactions. To improve the legibility and naturalness of obstacle avoidance in human crowded environments, we introduce a sampling-based local path planner, similar to the method used in autonomous driving cars. The key idea is to avoid obstacles by switching lanes. We also adopt a simple rule to decrease the number of unnecessary deviations from the reference path. Experiments carried out in real-world environments confirmed the effectiveness of the proposed algorithms.
本文描述了一种用于在医院环境中沿参考路径运送材料的差速驱动机器人的基于激光的人员检测和避障算法的开发。从激光数据中检测人员是在与人员共享工作空间中进行安全导航的重要功能。然而，传统方法通常在从各个聚类中提取的手工制作的几何特征上使用机器学习技术。此外，用于训练模型的数据集通常较小，并且需要手动标记每个激光扫描，这增加了在新环境中部署人员检测算法的难度和成本。为了解决这些问题，(1) 我们提出了一种新颖的基于深度学习的方法，该方法使用滑动窗口中的深度神经网络有效地对激光扫描的每个单点进行分类。(2) 为了在不降低性能的情况下提高推理速度，我们使用跳跃距离聚类方法来减少需要评估的点数。(3) 为了减少数据标注的工作量，我们还提出了一种自动标注实际场景中收集的数据的方法。总的来说，所提出的方法可以实时运行，并且性能明显优于传统方法。其次，传统的纯反应式避障算法在动态环境中可能会产生低效和振荡的行为，使行人感到困惑，并且可能导致危险的反应。为了提高在人多拥挤环境中的避障的可读性和自然性，我们引入了一种基于采样的局部路径规划器，类似于自动驾驶汽车中使用的方法。其关键思想是通过切换车道来避开障碍物。我们还采用了一个简单的规则来减少从参考路径不必要的偏离次数。在真实环境中进行的实验证实了所提出的算法的有效性。
