4 citations · 9 across the 13 of their papers we have counts for
9 papers
EdgeCalib: Multi-Frame Weighted Edge Features for Automatic Targetless LiDAR-Camera Calibration
Xingchen Li, Yifan Duan, Beibei Wang +5
In multimodal perception systems, achieving precise extrinsic calibration between LiDAR and camera is of critical importance. Previous calibration methods often required specific t…
PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning
Wenhao Yu, Jie Peng, Quecheng Qiu +3
Robot navigation using deep reinforcement learning (DRL) has shown great potential in improving the performance of mobile robots. Nevertheless, most existing DRL-based navigation m…
Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection
Yingjie Wang, Jiajun Deng, Yao Li +6
LiDAR and Radar are two complementary sensing approaches in that LiDAR specializes in capturing an object's 3D shape while Radar provides longer detection ranges as well as velocit…
: Transferring Visual Representations for Reinforcement Learning via Prompting
Guoliang You, Xiaomeng Chu, Yifan Duan +4
It is important for deep reinforcement learning (DRL) algorithms to transfer their learned policies to new environments that have different visual inputs. In this paper, we introdu…
Deep Reinforcement Learning for Localizability-Enhanced Navigation in Dynamic Human Environments
Yuan Chen, Quecheng Qiu, Xiangyu Liu +5
Reliable localization is crucial for autonomous robots to navigate efficiently and safely. Some navigation methods can plan paths with high localizability (which describes the capa…
TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching
Haojie Ren, Sha Zhang, Sugang Li +5
Recently, it has become popular to deploy sensors such as LiDARs on the roadside to monitor the passing traffic and assist autonomous vehicle perception. Unlike autonomous vehicle…