In this work, we have implemented a SLAM-assisted navigation module for a real autonomous vehicle with unknown dynamics. The navigation objective is to reach a desired goal configuration along a collision-free trajectory while adhering to the dynamics of the system. Specifically, we use LiDAR-based Hector SLAM for building the map of the environment, detecting obstacles, and for tracking vehicle's conformance to the trajectory as it passes through various states. For motion planning, we use rapidly exploring random trees (RRTs) on a set of generated motion primitives to search for dynamically feasible trajectory sequences and collision-free path to the goal. We demonstrate complex maneuvers such as parallel parking, perpendicular parking, and reversing motion by the real vehicle in a constrained environment using the presented approach.
翻译:在这项工作中,我们为一部具有未知动态的真正自主车辆实施了由SLAM协助的导航模块。导航目标是在遵守系统动态的同时沿无碰撞轨迹实现理想的目标配置。具体地说,我们利用以LIDAR为基地的Hector SLAM来绘制环境地图,探测障碍,并跟踪车辆在不同州行驶时与轨迹的一致性。在行动规划中,我们利用一系列生成的运动原始动力迅速探索随机树(RRTs),以寻找动态可行的轨迹序列和通往目标的无碰撞路径。我们展示了各种复杂的动作,如平行停车、侧翼泊车和在受限制的环境中使用实际车辆的倒车动作。