Planning Paths through Occlusions in Urban Environments

Yutao Han, Youya Xia, Guo-Jun Qi, Mark Campbell
Proceedings of The 6th Conference on Robot Learning, PMLR 205:266-275, 2023.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v205-han23a, title = {Planning Paths through Occlusions in Urban Environments}, author = {Han, Yutao and Xia, Youya and Qi, Guo-Jun and Campbell, Mark}, booktitle = {Proceedings of The 6th Conference on Robot Learning}, pages = {266--275}, year = {2023}, editor = {Liu, Karen and Kulic, Dana and Ichnowski, Jeff}, volume = {205}, series = {Proceedings of Machine Learning Research}, month = {14--18 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v205/han23a/han23a.pdf}, url = {https://proceedings.mlr.press/v205/han23a.html}, 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.} }
Endnote
%0 Conference Paper %T Planning Paths through Occlusions in Urban Environments %A Yutao Han %A Youya Xia %A Guo-Jun Qi %A Mark Campbell %B Proceedings of The 6th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2023 %E Karen Liu %E Dana Kulic %E Jeff Ichnowski %F pmlr-v205-han23a %I PMLR %P 266--275 %U https://proceedings.mlr.press/v205/han23a.html %V 205 %X 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.
APA
Han, Y., Xia, Y., Qi, G. & Campbell, M.. (2023). Planning Paths through Occlusions in Urban Environments. Proceedings of The 6th Conference on Robot Learning, in Proceedings of Machine Learning Research 205:266-275 Available from https://proceedings.mlr.press/v205/han23a.html.

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