Deep reinforcement learning (DRL) demonstrates great potential in mapless navigation domain. However, such a navigation model is normally restricted to a fixed configuration of the range sensor because its input format is fixed. In this paper, we propose a DRL model that can address range data obtained from different range sensors with different installation positions. Our model first extracts the goal-directed features from each obstacle point. Subsequently, it chooses global obstacle features from all point-feature candidates and uses these features for the final decision. As only a few points are used to support the final decision, we refer to these points as support points and our approach as support point-based navigation (SPN). Our model can handle data from different LiDAR setups and demonstrates good performance in simulation and real-world experiments. Moreover, it shows great potential in crowded scenarios with small obstacles when using a high-resolution LiDAR.
翻译:深度加固学习( DRL) 显示无地图导航域的巨大潜力。 但是, 这种导航模型通常局限于射程传感器的固定配置, 原因是其输入格式是固定的。 在本文中, 我们提议一个 DRL 模型, 可以处理从不同安装位置的不同射程传感器获得的射程数据。 我们的模型首先从每个障碍点提取目标导向的特征。 随后, 它从所有点性能候选人中选择全球障碍特征, 并将这些特征用于最终决定。 由于只有几个点用于支持最终决定, 我们将这些点作为支持点和我们的方法, 作为支持点基导航( SPN) 。 我们的模型可以处理来自不同LIDAR设置的数据, 并展示模拟和现实世界实验中的良好性能。 此外, 当使用高分辨率的LDAR 时, 它在拥挤的情景下展示出巨大的潜力, 且有小障碍 。