Planning Paths through Occlusions in Urban Environments

Yutao Han, Youya Xia, Guo-Jun Qi, Mark Campbell

Student First Author: no

Keywords: Navigation, Occluded Environments, Semantic Scene Understanding

TL;DR: Planning through occluded urban environments using image inpainting techniques.

Abstract:

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.

Code: https://github.com/genplanning/generative_planning