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 2025: Our paper BTSA, on dynamic LiDAR-inertial odometry, has been accepted to IEEE Robotics and Automation Letters (RA-L).
March 2025: Our paper GEODE, presenting a heterogeneous LiDAR dataset for benchmarking robust localization, has been accepted to The International Journal of Robotics Research (IJRR).
February 2025: Our paper CaRtGS, on Gaussian splatting SLAM, has been accepted to IEEE Robotics and Automation Letters (RA-L).
October 2024: Our paper S3E, presenting a multi-robot multimodal dataset for collaborative SLAM, has been accepted to IEEE Robotics and Automation Letters (RA-L).
January 2024: Our paper RELEAD, on resilient localization with degeneracy detection, has been accepted to ICRA 2024.
January 2024: Our paper CoLRIO, on centralized multi-agent LiDAR-inertial odometry, has been accepted to ICRA 2024.
December 2023: Our paper DCL-SLAM, on distributed collaborative LiDAR SLAM, has been accepted to IEEE Sensors Journal.

Publications

Breaking the Static Assumption: A Dynamic-Aware LIO Framework Via Spatio-Temporal Normal Analysis

Zhiqiang Chen, Cedric Le Gentil, Fuling Lin, Minghao Lu, Qiyuan Qiao, Bowen Xu, Yuhua Qi, Peng Lu

IEEE Robotics and Automation Letters (RA-L)

A dynamic-aware LiDAR-inertial odometry framework that integrates spatio-temporal normal analysis to achieve robust localization in challenging dynamic environments.

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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

IJRR

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.

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