High Precision Lidar
High-precision LiDAR research focuses on improving the accuracy and efficiency of 3D point cloud data acquisition and processing for various applications, particularly in robotics and autonomous driving. Current research emphasizes addressing motion distortion through LiDAR-IMU fusion and developing novel algorithms, including transformer networks and convolutional neural networks, for tasks like object detection, place recognition, and map building. These advancements are crucial for enhancing the reliability and robustness of perception systems in challenging environments, impacting fields ranging from autonomous navigation to urban mapping and environmental monitoring.
Papers
Boreas: A Multi-Season Autonomous Driving Dataset
Keenan Burnett, David J. Yoon, Yuchen Wu, Andrew Zou Li, Haowei Zhang, Shichen Lu, Jingxing Qian, Wei-Kang Tseng, Andrew Lambert, Keith Y. K. Leung, Angela P. Schoellig, Timothy D. Barfoot
Lunar Rover Localization Using Craters as Landmarks
Larry Matthies, Shreyansh Daftry, Scott Tepsuporn, Yang Cheng, Deegan Atha, R. Michael Swan, Sanjna Ravichandar, Masahiro Ono