论文标题
沿着类似的线：长期自治路径的局部避免
Along Similar Lines: Local Obstacle Avoidance for Long-term Autonomous Path Following
论文作者
论文摘要
Visual Teach和重复3（VT＆R3）（立体声VT＆R的概括）使用单个丰富的传感器流的叠层映射和定位来实现长期自主路径跟踪。在本文中，我们提高了VT＆R3的激光雷达实施的能力，以可靠地检测并避免在不断变化的环境中遇到障碍。我们的架构将障碍感问题简化为与位置相关的变化检测。然后，我们通过引入新的边缘成本度量与曲线计划空间来扩展基于通用样本的运动计划者的行为，以更好地适合教学和重复问题结构。最终的计划者会产生自然光滑的路径，从而避免局部障碍，同时最大程度地减少横向路径偏差以最佳利用先前的地形知识。当我们将该方法与VT＆R一起使用时，可以将其推广以适合任意路径遵循的应用程序。在差异驱动机器人上的在线运行时分析，单位测试和定性实验的实验结果显示了该技术在复杂的非结构化环境中可靠的长期自主操作的承诺。
Visual Teach and Repeat 3 (VT&R3), a generalization of stereo VT&R, achieves long-term autonomous path-following using topometric mapping and localization from a single rich sensor stream. In this paper, we improve the capabilities of a LiDAR implementation of VT&R3 to reliably detect and avoid obstacles in changing environments. Our architecture simplifies the obstacle-perception problem to that of place-dependent change detection. We then extend the behaviour of generic sample-based motion planners to better suit the teach-and-repeat problem structure by introducing a new edge-cost metric paired with a curvilinear planning space. The resulting planner generates naturally smooth paths that avoid local obstacles while minimizing lateral path deviation to best exploit prior terrain knowledge. While we use the method with VT&R, it can be generalized to suit arbitrary path-following applications. Experimental results from online run-time analysis, unit testing, and qualitative experiments on a differential drive robot show the promise of the technique for reliable long-term autonomous operation in complex unstructured environments.
