论文标题
学会使用不同的激光雷达配置来无地图：一种基于支持点的方法
Learn to Navigate Maplessly with Varied LiDAR Configurations: A Support Point-Based Approach
论文作者
论文摘要
深钢筋学习（DRL）在无地图导航域中表现出巨大的潜力。但是，这种导航模型通常仅限于范围传感器的固定配置，因为其输入格式是固定的。在本文中，我们提出了一个DRL模型，该模型可以解决从具有不同安装位置的不同范围传感器获得的范围数据。我们的模型首先从每个障碍点提取目标指导的特征。随后，它从所有点功能候选者中选择全球障碍物功能，并将这些功能用于最终决定。由于仅使用几点来支持最终决定，因此我们将这些点称为支持点，而我们的方法是基于支持点的导航（SPN）。我们的模型可以处理来自不同激光雷达设置的数据，并在模拟和现实世界实验中证明了良好的性能。此外，在使用高分辨率激光痛时，它在拥挤的情况下显示出很大的潜力。
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.
