  Reliable knowledge of road boundaries is critical for autonomous vehicle
navigation. We propose a robust curb detection and filtering technique based on
the fusion of camera semantics and dense lidar point clouds. The lidar point
clouds are collected by fusing multiple lidars for robust feature detection.
The camera semantics are based on a modified EfficientNet architecture which is
trained with labeled data collected from onboard fisheye cameras. The point
clouds are associated with the closest curb segment with $L_2$-norm analysis
after projecting into the image space with the fisheye model projection. Next,
the selected points are clustered using unsupervised density-based spatial
clustering to detect different curb regions. As new curb points are detected in
consecutive frames they are associated with the existing curb clusters using
temporal reachability constraints. If no reachability constraints are found a
new curb cluster is formed from these new points. This ensures we can detect
multiple curbs present in road segments consisting of multiple lanes if they
are in the sensors' field of view. Finally, Delaunay filtering is applied for
outlier removal and its performance is compared to traditional RANSAC-based
filtering. An objective evaluation of the proposed solution is done using a
high-definition map containing ground truth curb points obtained from a
commercial map supplier. The proposed system has proven capable of detecting
curbs of any orientation in complex urban road scenarios comprising straight
roads, curved roads, and intersections with traffic isles.
