Abstract
Curbs are used as physical markers to delimit roads and to redirect traffic into multiple directions (e.g., islands and roundabouts). Detection of road curbs is a fundamental task for autonomous vehicle navigation in urban environments. Since almost two decades, solutions that use various types of sensors, including vision, Light Detection and Ranging (LiDAR) sensors, among others, have emerged to address the curb detection problem. This survey elaborates on the advances of road curb detection problems, a research field that has grown over the last two decades and continues to be the ground for new theoretical and applied developments. We identify the tasks involved in the road curb detection methods and their applications on autonomous vehicle navigation and advanced driver assistance system (ADAS). Finally, we present an analysis on the similarities and differences of the wide variety of contributions.
Highlights
Over the last two decades, the road curb detection problem has attracted the attention of research teams around the world
Advanced driver assistance system (ADAS) is another important application for road curb detection methods since they provide the driver with information when the vehicle risks getting off the road, lane departure alerts, etc
Light Detection and Ranging (LiDAR) sensors provide either dense or sparse 3D data, the use of Digital Elevation Map became a common practice in the road curb detection problem [6,8–14]
Summary
Over the last two decades, the road curb detection problem has attracted the attention of research teams around the world. Advanced driver assistance system (ADAS) is another important application for road curb detection methods since they provide the driver with information when the vehicle risks getting off the road, lane departure alerts, etc. Road curb detection methods were based on vision systems and LiDAR sensors. Stereo vision systems were used to detect road curbs on dense 3D data [6–8]. Stereo vision-based methods introduced the idea of using a Digital Elevation Map (DEM) combined with classical edge detection methods and the Hough transform. LiDAR sensors provide either dense or sparse 3D data, the use of Digital Elevation Map became a common practice in the road curb detection problem [6,8–14]. To search for road curbs on sparse 3D data provided by LiDAR 3D sensors, ground segmentation [15–18] and feature extraction [15,17,19–30] have been widely discussed over the last decade.
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