One paper has been accepted to IEEE RA-L 2026
IEEE Robotics and Automation Letters (RA-L), launched by the IEEE Robotics & Automation Society in 2015, is a leading peer-reviewed archival journal that publishes timely and concise reports of innovative research in robotics and automation. According to the 2025 release of Journal Citation Reports, based on 2024 data, RA-L has a Journal Impact Factor of 5.3 and a 5-year Impact Factor of 6.0, and is ranked in Q1 in the Robotics category.
LiDAR-Anchored Collaborative Distillation for Robust 2D Representations
Authors: Wonjun Jo (POSTECH), Hyunwoo Ha (POSTECH), Kim Ji-Yeon (POSTECH), Hawook Jeong (RideFlux), Tae-Hyun Oh (KAIST)
As deep learning continues to advance, self-supervised learning has made considerable strides. It allows 2D image encoders to extract useful features for various downstream tasks, including those related to autonomous robotic perception systems. Nevertheless, pre-trained 2D image encoders fall short in conducting the task under noisy and adverse weather conditions beyond clear daytime scenes, which require for robust visual perception. To address these issues, we propose a novel self-supervised approach, Collaborative Distillation, which leverages 3D LiDAR as self-supervision to improve robustness to noisy and adverse weather conditions in 2D image encoders while retaining their original capabilities. Our method outperforms competing methods in various downstream tasks across diverse conditions and exhibits strong generalization ability. In addition, our method also improves 3D awareness stemming from LiDAR’s characteristics. This advancement highlights our method’s practicality and adaptability in real world scenarios.