RGB Camera and LiDAR Fusion for Road Detection - Candan, Arda Taha; Kalkan, Habil (Institute of Electrical and Electronics Engineers Inc., 2022)
Drivable road detection is one of the most fundamental problems for autonomous vehicles. Although good results can be obtained by using deep learning-based image segmentation methods on RGB camera images recently, the camera's dependence on ambient lighting increases the error rate in bad lighting conditions (such as dark environments, shadows, and reflections). Studies have shown that images obtained from cameras can produce better results when used with LiDARs by utilizing various fusion techniques. In this study, comparisons were made on U-Net model with early fusion by applying various fusion operations on RGB camera and LiDAR data based on the fusion methods in the literature. As a result of the study, sensor fusion approaches that give the best results in the field of driveable road detection were determined and inferences were made about how to improve success rates. © 2022 Elsevier B.V., All rights reserved.
Yol Tespiti Için RGB Kamera ve LiDAR Fuzyonu
2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022 -- Antalya; Akdeniz University -- 183936
Keywords: autonomous driving; computer vision; deep learning; road detection