Cited 6 time in webofscience Cited 8 time in scopus

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

Title
LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles
Authors
Kumar, Gurumadaiah AjayLee, Jin HeeHwang, JongrakPark, JaehyeongYoon, Sung HoonKwon, Soon
DGIST Authors
Kumar, Gurumadaiah Ajay; Lee, Jin Hee; Hwang, Jongrak; Park, Jaehyeong; Yoon, Sung Hoon; Kwon, Soon
Issue Date
2020-02
Citation
Symmetry, 12(2), 324
Type
Article
Article Type
Article
Author Keywords
computational geometry transformationprojectionsensor fusionself-driving vehiclesensor calibrationdepth sensingpoint cloud to image mappingautonomous vehicle
Keywords
EXTRINSIC CALIBRATION3D LIDARREGISTRATIONSYSTEM
ISSN
2073-8994
Abstract
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 lowlevel 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. © 2020 by the authors.
URI
http://hdl.handle.net/20.500.11750/11663
DOI
10.3390/sym12020324
Publisher
MDPI AG
Related Researcher
  • Author Kwon, Soon  
  • Research Interests computer vision; deep learning; autonomous driving; parallel processing; vision system on chip
Files:
Collection:
Division of Automotive Technology1. Journal Articles


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