  Reliable curb detection is critical for safe autonomous driving in urban
contexts. Curb detection and tracking are also useful in vehicle localization
and path planning. Past work utilized a 3D LiDAR sensor to determine accurate
distance information and the geometric attributes of curbs. However, such an
approach requires dense point cloud data and is also vulnerable to false
positives from obstacles present on both road and off-road areas. In this
paper, we propose an approach to detect and track curbs by fusing together data
from multiple sensors: sparse LiDAR data, a mono camera and low-cost ultrasonic
sensors. The detection algorithm is based on a single 3D LiDAR and a mono
camera sensor used to detect candidate curb features and it effectively removes
false positives arising from surrounding static and moving obstacles. The
detection accuracy of the tracking algorithm is boosted by using Kalman
filter-based prediction and fusion with lateral distance information from
low-cost ultrasonic sensors. We next propose a line-fitting algorithm that
yields robust results for curb locations. Finally, we demonstrate the practical
feasibility of our solution by testing in different road environments and
evaluating our implementation in a real vehicle\footnote{Demo video clips
demonstrating our algorithm have been uploaded to Youtube:
https://www.youtube.com/watch?v=w5MwsdWhcy4,
https://www.youtube.com/watch?v=Gd506RklfG8.}. Our algorithm maintains over
90\% accuracy within 4.5-22 meters and 0-14 meters for the KITTI dataset and
our dataset respectively, and its average processing time per frame is
approximately 10 ms on Intel i7 x86 and 100ms on NVIDIA Xavier board.
