A Framework for Road Boundary Detection based on Camera-LIDAR Fusion in World Coordinate System and Its Performance Evaluation Using Carla Simulator

Abstract

<p>Detection of road boundaries using camera-LIDAR fusion in world coordinate system poses several challenges. First, the pixels from the image need to be transformed into the world coordinate system, which is commonly done using inverse perspective mapping (IPM) technique. However, this technique is built based on assumptions that the road model is planar and rigid, assumptions that are not always held in real-world scenarios. Furthermore, obtaining the ground truth road boundaries is a difficult task for a real-world test, especially when the detection results are represented in world coordinate system. To solve the first challenge, this paper proposes a new framework for fusing camera and LIDAR data for detecting road boundaries using a transformation technique that can work on scenarios with varying and nonplanar roads. With this framework, we were able to fuse road boundary detection results that are extracted from camera and LIDAR separately. To overcome the second challenge, we used Carla Simulator and developed a method for extracting the ground truth points so that evaluation and comparison of various road boundary detection methods can be done properly. Based on our test results, the proposed transformation technique performed better than the widely used IPM technique. The results also show that our road boundary detection method can achieve error values below 0.3 m and reveal some key characteristics of camera and LIDAR-based detection method. Those characteristics were utilized to improve the fusion technique that is demonstrated in this paper.</p>