A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in Winter
Accurate and efficient environmental awareness is a fundamental capability of autonomous driving technology and the real-time data collected by sensors offer autonomous vehicles an intuitive impression of their environment. Unfortunately, the ambient noise caused by varying weather conditions immedi...
Main Authors: | Weiqi Wang, Xiong You, Lingyu Chen, Jiangpeng Tian, Fen Tang, Lantian Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-03-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/14/6/1468 |
Similar Items
-
ZUST Campus: A Lightweight and Practical LiDAR SLAM Dataset for Autonomous Driving Scenarios
by: Yuhang He, et al.
Published: (2024-04-01) -
Accurate Calibration of Multi-LiDAR-Multi-Camera Systems
by: Zoltán Pusztai, et al.
Published: (2018-07-01) -
LiDAR Point Cloud Generation for SLAM Algorithm Evaluation
by: Łukasz Sobczak, et al.
Published: (2021-05-01) -
Fast and Accurate Desnowing Algorithm for LiDAR Point Clouds
by: Ji-Il Park, et al.
Published: (2020-01-01) -
LiDAR-as-Camera for End-to-End Driving
by: Ardi Tampuu, et al.
Published: (2023-03-01)