107 citations · 472 across the 89 of their papers we have counts for
18 papers · 1 filter
Electric Vehicle Automatic Charging System Based on Vision-force Fusion
Dashun Guo, Liang Xie, Hongxiang Yu +2
Electric vehicles are an emerging means of transportation with environmental friendliness. The automatic charging is a hot topic in this field that is full of challenges. We introd…
Learning Stereopsis from Geometric Synthesis for 6D Object Pose Estimation
Jun Wu, Lilu Liu, Yue Wang +1
Current monocular-based 6D object pose estimation methods generally achieve less competitive results than RGBD-based methods, mostly due to the lack of 3D information. To make up t…
Domain Generalization for Vision-based Driving Trajectory Generation
Yunkai Wang, Dongkun Zhang, Yuxiang Cui +5
One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for visi…
HiTMap: A Hierarchical Topological Map Representation for Navigation in Unknown Environments
Xuecheng Xu, Cheng Wang, Yue Wang +1
The ability to autonomously navigate in unknown environments is important for mobile robots. The map is the core component to achieve this. Most map representations rely on drift-f…
Learning Observation-Based Certifiable Safe Policy for Decentralized Multi-Robot Navigation
Yuxiang Cui, Longzhong Lin, Xiaolong Huang +3
Safety is of great importance in multi-robot navigation problems. In this paper, we propose a control barrier function (CBF) based optimizer that ensures robot safety with both hig…
A Right Invariant Extended Kalman Filter for Object based SLAM
Yang Song, Zhuqing Zhang, Jun Wu +3
With the recent advance of deep learning based object recognition and estimation, it is possible to consider object level SLAM where the pose of each object is estimated in the SLA…
Socially-Aware Multi-Agent Following with 2D Laser Scans via Deep Reinforcement Learning and Potential Field
Yuxiang Cui, Xiaolong Huang, Yue Wang +1
Target following in dynamic pedestrian environments is an important task for mobile robots. However, it is challenging to keep tracking the target while avoiding collisions in crow…
Learning Interpretable BEV Based VIO without Deep Neural Networks
Zexi Chen, Haozhe Du, Xuecheng Xu +3
Monocular visual-inertial odometry (VIO) is a critical problem in robotics and autonomous driving. Traditional methods solve this problem based on filtering or optimization. While…
Efficient Object Manipulation to an Arbitrary Goal Pose: Learning-based Anytime Prioritized Planning
Kechun Xu, Hongxiang Yu, Renlang Huang +3
We focus on the task of object manipulation to an arbitrary goal pose, in which a robot is supposed to pick an assigned object to place at the goal position with a specific orienta…
Anti-degenerated UWB-LiDAR Localization for Automatic Road Roller in Tunnel
Bingqi Shen, Yuyin Chen, Huiyong Yang +5
The automatic road roller, as a popular type of construction robot, has attracted much interest from both the industry and the research community in recent years. However, when it…
Improved Radar Localization on Lidar Maps Using Shared Embedding
Huan Yin, Yue Wang, Rong Xiong
We present a heterogeneous localization framework for solving radar global localization and pose tracking on pre-built lidar maps. To bridge the gap of sensing modalities, deep neu…
Toward Consistent Drift-free Visual Inertial Localization on Keyframe Based Map
Zhuqing Zhang, Yanmei Jiao, Shoudong Huang +2
Global localization is essential for robots to perform further tasks like navigation. In this paper, we propose a new framework to perform global localization based on a filter-bas…
Neural Motion Prediction for In-flight Uneven Object Catching
Hongxiang Yu, Dashun Guo, Huan Yin +4
In-flight objects capture is extremely challenging. The robot is required to complete trajectory prediction, interception position calculation and motion planning in sequence withi…
Learn to Differ: Sim2Real Small Defection Segmentation Network
Zexi Chen, Zheyuan Huang, Yunkai Wang +3
Recent studies on deep-learning-based small defection segmentation approaches are trained in specific settings and tend to be limited by fixed context. Throughout the training, the…
Kinematic Motion Retargeting via Neural Latent Optimization for Learning Sign Language
Haodong Zhang, Weijie Li, Jiangpin Liu +4
Motion retargeting from a human demonstration to a robot is an effective way to reduce the professional requirements and workload of robot programming, but faces the challenges res…
Efficient learning of goal-oriented push-grasping synergy in clutter
Kechun Xu, Hongxiang Yu, Qianen Lai +2
We focus on the task of goal-oriented grasping, in which a robot is supposed to grasp a pre-assigned goal object in clutter and needs some pre-grasp actions such as pushes to enabl…
Collaborative Recognition of Feasible Region with Aerial and Ground Robots through DPCN
Yunshuang Li, Zheyuan Huang, Zexi chen +2
Ground robots always get collision in that only if they get close to the obstacles, can they sense the danger and take actions, which is usually too late to avoid the crash, causin…
Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning
Huan Yin, Xuecheng Xu, Yue Wang +1
Place recognition is critical for both offline mapping and online localization. However, current single-sensor based place recognition still remains challenging in adverse conditio…