论文标题
使用预测的占用地图的高速机器人导航
High-Speed Robot Navigation using Predicted Occupancy Maps
论文作者
论文摘要
安全和高速导航是机器人系统现实世界部署的关键功能。现有方法的一个重要局限性是与现有传感器技术的显式映射和有限的视野（FOV）相关的计算瓶颈。在本文中，我们研究了算法方法，这些方法使机器人可以预测在传感器范围之外延伸的空间，以在高速下进行稳健的计划。我们使用从现实世界数据训练的无需人类注释标签的生成神经网络来实现这一目标。此外，我们扩展了现有的控制算法，以支持利用预测的空间以高速改善无碰撞计划和导航。我们的实验是使用RGBD传感器基于MIT赛车在物理机器人上进行的，与未在地图预测区域运行的控制器相比，能够以4 m/s的形式证明性能提高。
Safe and high-speed navigation is a key enabling capability for real world deployment of robotic systems. A significant limitation of existing approaches is the computational bottleneck associated with explicit mapping and the limited field of view (FOV) of existing sensor technologies. In this paper, we study algorithmic approaches that allow the robot to predict spaces extending beyond the sensor horizon for robust planning at high speeds. We accomplish this using a generative neural network trained from real-world data without requiring human annotated labels. Further, we extend our existing control algorithms to support leveraging the predicted spaces to improve collision-free planning and navigation at high speeds. Our experiments are conducted on a physical robot based on the MIT race car using an RGBD sensor where were able to demonstrate improved performance at 4 m/s compared to a controller not operating on predicted regions of the map.
