Curb Detection and Tracking in Low-Resolution 3D Point Clouds Based on Optimization Framework

Curb detection and tracking is an essential component of autonomous vehicle operation in urban environments. Detecting curbs is a particularly challenging task in urban environments that contain countless dynamic objects. Previous studies have approached curb detection using different types of sensor such as cameras, radar, and LIDAR. Among these, LIDAR sensors have superior advantages in regard to detecting curbs because of their robustness in different weather conditions and they can provide accurate distance measurements. Previous methods based on LIDAR have exploited high-resolution 3D point clouds using high-cost LIDARs. However, the processing of large volumes of information is inefficient for autonomous driving technologies because of real time constraints. This paper presents a novel real-time curb detection and tracking algorithm that makes use of a low-resolution LIDAR. The proposed method consists of three steps. First is the extraction of curb candidates based on Principal Component Analysis (PCA) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Second is the selection of the optimal candidate using an optimization framework. Lastly is the tracking of the detected curbs. In the tracking module, we use a combination of spatial consistency and validation gate to track the curb in the occlusion region with dynamic objects. Experiments on a public dataset show that the proposed method achieved 91.54% and 89.76% F
₁
score on the straight and curved road while running at about 18 ms per frame, thereby outperforming the state-of-the-art by a large margin. In addition, we integrated the proposed method with the localization module of our autonomous driving platform. The localization module integrated with the proposed method reduces the positional and lateral root mean square (RMS) error of the vehicle localization by 3.58% and 6.68% respectively. In addition, we compare this method with a deep neural network based method from the perspective of a safety-critical system such as self-driving cars.