12 citations · 35 across the 26 of their papers we have counts for
6 papers · 1 filter
How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization
Hai Zhang, Hang Yu, Junqiao Zhao +5
Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high couplin…
VNI-Net: Vector Neurons-based Rotation-Invariant Descriptor for LiDAR Place Recognition
Gengxuan Tian, Junqiao Zhao, Yingfeng Cai +3
LiDAR-based place recognition plays a crucial role in Simultaneous Localization and Mapping (SLAM) and LiDAR localization. Despite the emergence of various deep learning-based and…
LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation
Kai Huang, Junqiao Zhao, Zhongyang Zhu +2
Local geometric information, i.e. normal and distribution of points, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constraints for dat…
Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery
Xiao Zhang, Hai Zhang, Hongtu Zhou +4
Safety is one of the main challenges in applying reinforcement learning to realistic environmental tasks. To ensure safety during and after training process, existing methods tend…
Learning Sequence Descriptor based on Spatio-Temporal Attention for Visual Place Recognition
Junqiao Zhao, Fenglin Zhang, Yingfeng Cai +4
Visual Place Recognition (VPR) aims to retrieve frames from a geotagged database that are located at the same place as the query frame. To improve the robustness of VPR in perceptu…
LIMOT: A Tightly-Coupled System for LiDAR-Inertial Odometry and Multi-Object Tracking
Zhongyang Zhu, Junqiao Zhao, Kai Huang +3
Simultaneous localization and mapping (SLAM) is critical to the implementation of autonomous driving. Most LiDAR-inertial SLAM algorithms assume a static environment, leading to un…