Hello, I'm Zhiqiang Chen

I am currently pursuing a Ph.D. at The University of Hong Kong (HKU). I completed both my bachelor’s and master’s degrees with honors at Sun Yat-sen University.

My research focuses on autonomy in intelligent mobile systems, with a particular interest in perception in dynamic environments for robotic interaction. This involves robust localization in complex and degenerate scenarios, constructing detailed geometric and semantic models, scene abstraction and understanding, and lifelong learning for autonomous mapping in unfamiliar settings.

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News

October 2024: S3E, our paper on multi-Robot multimodal dataset for collaborative SLAM, has benn accepted to IEEE Robotics and Automation Letters (RA-L).
January 2024: RELEAD, our paper on resilient localization, has been accepted to ICRA, 2024.
January 2024: CoLRIO, our paper on centralized LiDAR SLAM, has been accepted to ICRA, 2024.
December 2023: DCL-SLAM, our paper on distributed LiDAR SLAM, has been accepted to IEEE Sensors Journal.

Publications

Heterogeneous LiDAR Dataset for Benchmarking Robust Localization in Diverse Degenerate Scenarios

Zhiqiang Chen, Yuhua Qi, Dapeng Feng, Xuebin Zhuang, Hongbo Chen, Xiangcheng Hu, Jin Wu, Kelin Peng, Peng Lu

Under Review

A comprehensive multi-LiDAR, multi-scenario dataset that extensively incorporates segments of real-world geometric degeneracy.

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S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM

Dapeng Feng, Yuhua Qi, Shipeng Zhong, Zhiqiang Chen, Qiming Chen, Hongbo Chen, Jin Wu, Jun Ma

IEEE Robotics and Automation Letters (RA-L)

The S3E dataset offers a comprehensive collection of multi-robot, multimodal data, capturing diverse cooperative trajectories in various environments, both outdoor and indoor.

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RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments

Zhiqiang Chen, Hongbo Chen, Yuhua Qi, Shipeng Zhong, Dapeng Feng, Jin Wu, Weisong Wen, Ming Liu

ICRA, 2024

A LiDAR-centric localization and mapping approach that enhances the accuracy and resilience of SLAM methods across diverse environments, particularly in scenarios with LiDAR degradation.

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CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms

Shipeng Zhong, Hongbo Chen, Yuhua Qi, Dapeng Feng, Zhiqiang Chen, Jin Wu, Weisong Wen, Ming Liu

ICRA, 2024

A centralized system to facilitate collaborative LiDAR-ranging-inertial state estimation, enabling robotic swarms to operate without the need for anchor deployment, and demonstrating significant enhancements in the accuracy of collaborative SLAM estimates.

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DCL-SLAM: A Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm

Shipeng Zhong, Yuhua Qi, Zhiqiang Chen, Jin Wu, Hongbo Chen, Ming Liu

IEEE Sensors Journal, 2024

DCL-SLAM is presented, a front-end agnostic fully distributed collaborative Light Detection And Ranging SLAM framework to co-localize in an unknown environment with low information exchange and achieves higher accuracy and lower bandwidth than other state-of-the-art multirobot LiDAR SLAM systems.

Project Page PDF arXiv Code