首页|New Findings from Ulm University in the Area of Robotics and Automation Describe d (Label-efficient Semantic Segmentation of Lidar Point Clouds In Adverse Weathe r Conditions)
New Findings from Ulm University in the Area of Robotics and Automation Describe d (Label-efficient Semantic Segmentation of Lidar Point Clouds In Adverse Weathe r Conditions)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Robotic s - Robotics and Automation. According to news originating from Ulm, Germany, by NewsRx correspondents, research stated, “Adverse weather conditions can severel y affect the performance of LiDAR sensors by introducing unwanted noise in the m easurements. Therefore, differentiating between noise and valid points is crucia l for the reliable use of these sensors.” Our news journalists obtained a quote from the research from Ulm University, “Cu rrent approaches for detecting adverse weather points require large amounts of l abeled data, which can be difficult and expensive to obtain. This letter propose s a label-efficient approach to segment LiDAR point clouds in adverse weather. W e develop a framework that uses few-shot semantic segmentation to learn to segme nt adverse weather points from only a few labeled examples. Then, we use a semi- supervised learning approach to generate pseudo-labels for unlabelled point clou ds, significantly increasing the amount of training data without requiring any a dditional labeling. We also integrate good weather data in our training pipeline , allowing for high performance in both good and adverse weather conditions. Res ults on real and synthetic datasets show that our method performs well in detect ing snow, fog, and spray.”
UlmGermanyEuropeRobotics and Autom ationRoboticsUlm University
NETL
NSTL
