LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles
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 o...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-02-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/12/2/324 |
| _version_ | 1811187841181417472 |
|---|---|
| author | G Ajay Kumar Jin Hee Lee Jongrak Hwang Jaehyeong Park Sung Hoon Youn Soon Kwon |
| author_facet | G Ajay Kumar Jin Hee Lee Jongrak Hwang Jaehyeong Park Sung Hoon Youn Soon Kwon |
| author_sort | G Ajay Kumar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T14:09:19Z |
| format | Article |
| id | doaj.art-74b9aecca5724df38fba6a0b7ea02a19 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T14:09:19Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-74b9aecca5724df38fba6a0b7ea02a192022-12-22T04:19:46ZengMDPI AGSymmetry2073-89942020-02-0112232410.3390/sym12020324sym12020324LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving VehiclesG Ajay Kumar0Jin Hee Lee1Jongrak Hwang2Jaehyeong Park3Sung Hoon Youn4Soon Kwon5Division of Automotive Technology, DGIST, Daegu-42988, KoreaDivision of Automotive Technology, DGIST, Daegu-42988, KoreaDivision of Automotive Technology, DGIST, Daegu-42988, KoreaDivision of Automotive Technology, DGIST, Daegu-42988, KoreaDivision of Automotive Technology, DGIST, Daegu-42988, KoreaDivision of Automotive Technology, DGIST, Daegu-42988, KoreaThe 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.https://www.mdpi.com/2073-8994/12/2/324computational geometry transformationprojectionsensor fusionself-driving vehiclesensor calibrationdepth sensingpoint cloud to image mappingautonomous vehicle |
| spellingShingle | G Ajay Kumar Jin Hee Lee Jongrak Hwang Jaehyeong Park Sung Hoon Youn Soon Kwon LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles Symmetry computational geometry transformation projection sensor fusion self-driving vehicle sensor calibration depth sensing point cloud to image mapping autonomous vehicle |
| title | LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles |
| title_full | LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles |
| title_fullStr | LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles |
| title_full_unstemmed | LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles |
| title_short | LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles |
| title_sort | lidar and camera fusion approach for object distance estimation in self driving vehicles |
| topic | computational geometry transformation projection sensor fusion self-driving vehicle sensor calibration depth sensing point cloud to image mapping autonomous vehicle |
| url | https://www.mdpi.com/2073-8994/12/2/324 |
| work_keys_str_mv | AT gajaykumar lidarandcamerafusionapproachforobjectdistanceestimationinselfdrivingvehicles AT jinheelee lidarandcamerafusionapproachforobjectdistanceestimationinselfdrivingvehicles AT jongrakhwang lidarandcamerafusionapproachforobjectdistanceestimationinselfdrivingvehicles AT jaehyeongpark lidarandcamerafusionapproachforobjectdistanceestimationinselfdrivingvehicles AT sunghoonyoun lidarandcamerafusionapproachforobjectdistanceestimationinselfdrivingvehicles AT soonkwon lidarandcamerafusionapproachforobjectdistanceestimationinselfdrivingvehicles |