A5054273684- Robotics and Sensor-Based Localization
- 3D Surveying and Cultural Heritage
- Remote Sensing and LiDAR Applications
- Advanced Optical Sensing Technologies
- Vehicle emissions and performance
- Advanced Neural Network Applications
- Vehicular Ad Hoc Networks (VANETs)
- Traffic Prediction and Management Techniques
- Autonomous Vehicle Technology and Safety
- Winter Sports Injuries and Performance
Chang'an University
2022-2024
Rain and snowfall will increase noise, change the resolution of objects in point cloud present great challenges to accurate recognition traffic objects. Accordingly, this article proposes an efficient real-time method for roadside 3-D light detection ranging (LIDAR) background extraction object segmentation under snowy weather. We first use a historical sequence quickly construct model, extract from current frame by using difference update model real-time. Then, noise caused non-background...
This paper proposes a new method to extract background and segment targets from point clouds collected by three-dimensional roadside LiDAR in snowfall weather. Background extraction target segmentation are two main problems environmental perception based on LiDAR. first introduces filtering algorithm, which uses the historical cloud sequence construct model real time filter difference. Then, non-background algorithm is proposed, including linear density for snow noise hierarchical clustering...