3D LIDAR based on FPCB mirror

Mechatronics ◽  
2022 ◽  
Vol 82 ◽  
pp. 102720
Author(s):  
Trevor S. Tai ◽  
Hui Zuo ◽  
Siyuan He
Keyword(s):  
3D Lidar

scholarly journals Robust Curb Detection with Fusion of 3D-Lidar and Camera Data

Sensors ◽  
2014 ◽  
Vol 14 (5) ◽  
pp. 9046-9073 ◽  
Author(s):  
Jun Tan ◽  
Jian Li ◽  
Xiangjing An ◽  
Hangen He
Keyword(s):  
3D Lidar

scholarly journals Real-time Track Obstacle Detection from 3D LIDAR Point Cloud

2021 ◽  
Vol 1910 (1) ◽  
pp. 012002
Author(s):  
Chao He ◽  
Jiayuan Gong ◽  
Yahui Yang ◽  
Dong Bi ◽  
Jianpin Lan ◽  
...  
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Obstacle Detection ◽  
3D Lidar

Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods

2021 ◽  
pp. 1-19
Author(s):  
Biao Gao ◽  
Yancheng Pan ◽  
Chengkun Li ◽  
Sibo Geng ◽  
Huijing Zhao
Keyword(s):  
Semantic Segmentation ◽  
Lidar Data ◽  
3D Lidar

3D LiDAR-GPS/IMU Calibration Based on Hand-Eye Calibration Model for Unmanned Vehicle

2020 ◽  
Author(s):  
Chen Chen ◽  
Guangming Xiong ◽  
Zhehua Zhang ◽  
Jianwei Gong ◽  
Jianyong Qi ◽  
...  
Keyword(s):  
Calibration Model ◽  
Unmanned Vehicle ◽  
3D Lidar

Water Hazard Detection Based on 3D LIDAR

2014 ◽  
Vol 668-669 ◽  
pp. 1174-1177 ◽  
Author(s):  
Hai Yan Shao ◽  
Zhen Hai Zhang ◽  
Ke Jie Li ◽  
Jian Wang ◽  
Tao Xu ◽  
...  
Keyword(s):  
Unmanned Ground Vehicle ◽  
Data Format ◽  
Ground Vehicle ◽  
Air Water Interface ◽  
Water Hazard ◽  
Outdoor Environments ◽  
Bodies Of Water ◽  
3D Lidar ◽  
Water Hazards ◽  
Complicated Task

Autonomous off-road navigation is a highly complicated task for a robot or unmanned ground vehicle (UGV) owing to the different kinds of obstacles it could encounter. In-particular, water hazards such as puddles and ponds are very common in outdoor environments and are hard to detect even with ranging devices due to the specular nature of reflection at the air water interface. In recent years, many researches to detect the water bodies have been done. But there still has been very little work on detecting bodies of water that could be navigation hazards, especially at night. In this paper, we used Velodyne HDL-64ES2 3D LIDAR to detect water hazard. The approach first analyzes the data format and transformation of 3D LIDAR, and then writes the data acquisition and visualizations algorithm, integrated data based on ICP algorithm. Finally according the intensity distribution identifies the water hazard. Experiments are carried out on the experimental car in campus, and results show the promising performance.

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