For autonomous robots navigating in urban environments, it is important for the robot to stay on the designated path of travel (i.e., the footpath), and avoid areas such as grass and garden beds, for safety and social conformity considerations. This paper presents an autonomous navigation approach for unknown urban environments that combines the use of semantic segmentation and LiDAR data. The proposed approach uses the segmented image mask to create a 3D obstacle map of the environment, from which, the boundaries of the footpath is computed. Compared to existing methods, our approach does not require a pre-built map and provides a 3D understanding of the safe region of travel, enabling the robot to plan any path through the footpath. Experiments comparing our method with two alternatives using only LiDAR or only semantic segmentation show that overall our proposed approach performs significantly better with greater than 91% success rate outdoors, and greater than 66% indoors. Our method enabled the robot to remain on the safe path of travel at all times, and reduced the number of collisions.
翻译:对于在城市环境中航行的自主机器人来说,重要的是机器人必须留在指定的旅行路径上(即徒步路径),并避免草地和花园床等区域,以便安全和社会符合性考虑。本文件介绍了对未知城市环境的一种自主导航方法,这种方法结合了语义分解和LiDAR数据的使用。拟议方法使用分层图像遮罩来绘制环境障碍图3D,从中计算行走路径的界限。与现有方法相比,我们的方法不需要预先建造的地图,而是提供对安全旅行区域的3D理解,使机器人能够规划任何行走路径。将我们的方法与仅使用LiDAR或仅使用语义分解的两种替代方法进行比较的实验表明,我们拟议方法的总体效果要好得多,户外成功率超过91%,室内成功率超过66%。我们的方法使得机器人能够始终留在安全的旅行道路上,并减少了碰撞次数。