Planning Paths through Occlusions in Urban Environments
Planning Paths through Occlusions in Urban Environments
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DOI：
10.48550/arxiv.2212.14138
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Yutao Han;Youya Xia;Guo-Jun Qi;Mark E. Campbell
中科院分区：
文献类型：
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作者：
Yutao Han;Youya Xia;Guo-Jun Qi;Mark E. Campbell
本文提出了一个在未知和封闭的城市空间中进行规划的新框架。我们特别关注遮挡严重影响通航的转弯和交叉口。该方法使用修补模型来填充稀疏的、遮挡的、语义的激光雷达点云，并为车辆规划动态可行的路径，以便车辆在开放和修补的空间中穿行。我们使用实时遮挡的汽车激光雷达数据演示了我们的方法，并表明通过修复遮挡区域，我们可以规划更长的路径，与不修复相比，具有更多的转弯选项；此外，与其他最先进的方法相比，我们的方法更接近于从没有遮挡的规划器获得的路径(称为地面真相)。
This paper presents a novel framework for planning in unknown and occluded urban spaces. We specifically focus on turns and intersections where occlusions significantly impact navigability. Our approach uses an inpainting model to fill in a sparse, occluded, semantic lidar point cloud and plans dynamically feasible paths for a vehicle to traverse through the open and inpainted spaces. We demonstrate our approach using a car's lidar data with real-time occlusions, and show that by inpainting occluded areas, we can plan longer paths, with more turn options compared to without inpainting; in addition, our approach more closely follows paths derived from a planner with no occlusions (called the ground truth) compared to other state of the art approaches.