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Article

LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles

Division of Automotive Technology, DGIST, Daegu-42988, Korea
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Symmetry 2020, 12(2), 324; https://doi.org/10.3390/sym12020324
Received: 3 January 2020 / Revised: 11 February 2020 / Accepted: 19 February 2020 / Published: 24 February 2020
The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation, and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the detection of objects at short and long distances. As both the sensors are capable of capturing the different attributes of the environment simultaneously, the integration of those attributes with an efficient fusion approach greatly benefits the reliable and consistent perception of the environment. This paper presents a method to estimate the distance (depth) between a self-driving car and other vehicles, objects, and signboards on its path using the accurate fusion approach. Based on the geometrical transformation and projection, low-level sensor fusion was performed between a camera and LiDAR using a 3D marker. Further, the fusion information is utilized to estimate the distance of objects detected by the RefineDet detector. Finally, the accuracy and performance of the sensor fusion and distance estimation approach were evaluated in terms of quantitative and qualitative analysis by considering real road and simulation environment scenarios. Thus the proposed low-level sensor fusion, based on the computational geometric transformation and projection for object distance estimation proves to be a promising solution for enabling reliable and consistent environment perception ability for autonomous vehicles. View Full-Text
Keywords: computational geometry transformation; projection; sensor fusion; self-driving vehicle; sensor calibration; depth sensing; point cloud to image mapping; autonomous vehicle computational geometry transformation; projection; sensor fusion; self-driving vehicle; sensor calibration; depth sensing; point cloud to image mapping; autonomous vehicle
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MDPI and ACS Style

Kumar, G.A.; Lee, J.H.; Hwang, J.; Park, J.; Youn, S.H.; Kwon, S. LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles. Symmetry 2020, 12, 324. https://doi.org/10.3390/sym12020324

AMA Style

Kumar GA, Lee JH, Hwang J, Park J, Youn SH, Kwon S. LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles. Symmetry. 2020; 12(2):324. https://doi.org/10.3390/sym12020324

Chicago/Turabian Style

Kumar, G Ajay, Jin Hee Lee, Jongrak Hwang, Jaehyeong Park, Sung Hoon Youn, and Soon Kwon. 2020. "LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles" Symmetry 12, no. 2: 324. https://doi.org/10.3390/sym12020324

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