Automatic Obstacle Classification using Laser and Camera Fusion

Abstract

State of the art Driving Assistance Systems and Autonomous Driving applications are employing sensor fusion in order to achieve trustable obstacle detection and classification under any meteorological and illumination condition. Fusion between laser and camera is widely used in ADAS applications in order to overcome the difficulties and limitations inherent to each of the sensors. In the system presented, some novel techniques for automatic and unattended data alignment are used and laser point clouds are exploited using Artificial Intelligence techniques to improve the reliability of the obstacle classification. New approaches to the problem of clustering sparse point clouds have been adopted, maximizing the information obtained from low resolution lasers. After improving cluster detection, AI techniques have been used to classify the obstacle not only with vision, but also with laser information. The fusion of the information acquired from both sensors, adding the classification capabilities of the laser, improves the reliability of the system.