11 citations · 29 across the 42 of their papers we have counts for
10 papers · 1 filter
CalibFormer: A Transformer-based Automatic LiDAR-Camera Calibration Network
Yuxuan Xiao, Yao Li, Chengzhen Meng +3
The fusion of LiDARs and cameras has been increasingly adopted in autonomous driving for perception tasks. The performance of such fusion-based algorithms largely depends on the ac…
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…
OCC-VO: Dense Mapping via 3D Occupancy-Based Visual Odometry for Autonomous Driving
Heng Li, Yifan Duan, Xinran Zhang +3
Visual Odometry (VO) plays a pivotal role in autonomous systems, with a principal challenge being the lack of depth information in camera images. This paper introduces OCC-VO, a no…
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…
USTC FLICAR: A Sensors Fusion Dataset of LiDAR-Inertial-Camera for Heavy-duty Autonomous Aerial Work Robots
Ziming Wang, Yujiang Liu, Yifan Duan +5
In this paper, we present the USTC FLICAR Dataset, which is dedicated to the development of simultaneous localization and mapping and precise 3D reconstruction of the workspace for…
: 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…
OA-DET3D: Embedding Object Awareness as a General Plug-in for Multi-Camera 3D Object Detection
Xiaomeng Chu, Jiajun Deng, Jianmin Ji +3
The recent advance in multi-camera 3D object detection is featured by bird's-eye view (BEV) representation or object queries. However, the ill-posed transformation from image-plane…