# Online Inference and Detection of Curbs in Partially Occluded Scenes with Sparse LIDAR

arXiv: https://arxiv.org/abs/1907.05375 · HTML: https://mlsanity.com/paper/1907.05375/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [CurbNet: Curb Detection Framework Based on LiDAR Point Cloud Segmentation](https://mlsanity.com/paper/2403.16794/) (2024)
- [How to Build a Curb Dataset with LiDAR Data for Autonomous Driving](https://mlsanity.com/paper/2110.03968/) (2021)
- [LiDAR-based curb detection for ground truth annotation in automated driving validation](https://mlsanity.com/paper/2312.00534/) (2023)
- [The Oxford Road Boundaries Dataset](https://mlsanity.com/paper/2106.08983/) (2021)

## Abstract (arXiv)

Road boundaries, or curbs, provide autonomous vehicles with essential information when interpreting road scenes and generating behaviour plans. Although curbs convey important information, they are difficult to detect in complex urban environments (in particular in comparison to other elements of the road such as traffic signs and road markings). These difficulties arise from occlusions by other traffic participants as well as changing lighting and/or weather conditions. Moreover, road boundaries have various shapes, colours and structures while motion planning algorithms require accurate and precise metric information in real-time to generate their plans. In this paper, we present a real-time LIDAR-based approach for accurate curb detection around the vehicle (360 degree). Our approach deals with both occlusions from traffic and changing environmental conditions. To this end, we project 3D LIDAR pointcloud data into 2D bird's-eye view images (akin to Inverse Perspective Mapping). These images are then processed by trained deep networks to infer both visible and occluded road boundaries. Finally, a post-processing step filters detected curb segments and tracks them over time. Experimental results demonstrate the effectiveness of the proposed approach on real-world driving data. Hence, we believe that our LIDAR-based approach provides an efficient and effective way to detect visible and occluded curbs around the vehicles in challenging driving scenarios.