no code implementations • 20 Apr 2023 • Chenglu Sun, Yichi Zhang, Yu Zhang, Ziling Lu, Jingbin Liu, Sijia Xu, Weidong Zhang
We propose asymmetric-evolution training (AET), a novel multi-agent reinforcement learning framework that can train multiple kinds of agents simultaneously in AMP game.
no code implementations • RA-L 2022 • Fuqiang Gu, Yong Lee, Yuan Zhuang, You Li, Jingbin Liu, Fangwen Yu, Ruiyuan Li, Chao Chen
Event-based sensors (e. g., DVS cameras) are capable of higher dynamic range, higher temporal resolution, lower time latency, and better power efficiency compared to conventional devices (e. g., RGB cameras).
no code implementations • 27 Oct 2020 • Hao Ma, Jingbin Liu, Zhirong Hu, Hongyu Qiu, Dong Xu, Zemin Wang, Xiaodong Gong, Sheng Yang
This paper designs a technique route to generate high-quality panoramic image with depth information, which involves two critical research hotspots: fusion of LiDAR and image data and image stitching.
no code implementations • 27 Oct 2020 • Hao Ma, Jingbin Liu, Keke Liu, Hongyu Qiu, Dong Xu, Zemin Wang, Xiaodong Gong, Sheng Yang
Registration of 3D LiDAR point clouds with optical images is critical in the combination of multi-source data.
1 code implementation • 30 Apr 2020 • Baichuan Huang, Hongwei Yi, Can Huang, Yijia He, Jingbin Liu, Xiao Liu
To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences.
1 code implementation • 21 Apr 2020 • Baichuan Huang, Hongwei Yi, Can Huang, Yijia He, Jingbin Liu, Xiao Liu
To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences.
1 code implementation • 6 Jan 2020 • Deyu Yin, Qian Zhang, Jingbin Liu, Xinlian Liang, Yunsheng Wang, Jyri Maanpää, Hao Ma, Juha Hyyppä, Ruizhi Chen
As an important technology in 3D mapping, autonomous driving, and robot navigation, LiDAR odometry is still a challenging task.
no code implementations • 20 Dec 2019 • Jingbin Liu, Shuai Liu, Xinyang Gu
Deep Q Network (DQN) is a very successful algorithm, yet the inherent problem of reinforcement learning, i. e. the exploit-explore balance, remains.
no code implementations • 2 Dec 2019 • Jingbin Liu, Xinyang Gu, Shuai Liu
We introduce a local action variance for policy network and find it can work collaboratively with the idea of entropy regularization.
no code implementations • 30 Aug 2019 • Jingbin Liu, Xinyang Gu, Shuai Liu
We propose an agent framework that integrates off-policy reinforcement learning with world model learning, so as to embody the important features of intelligence in our algorithm design.
no code implementations • 24 Aug 2019 • Baichuan Huang, Jun Zhao, Jingbin Liu
The paper makes an overview in SLAM including Lidar SLAM, visual SLAM, and their fusion.
Robotics Simultaneous Localization and Mapping