Chen Feng

Chen (Albert) Feng (冯宸)

I am a Ph.D. candidate in UAV Group, ECE at HKUST, supervised by Prof. Shaojie Shen. Prior to HKUST, I obtained a B.Eng from Harbin Institute of Technology and majored in Mechatronics Engineering (ME) in 2021.

I was a research intern from May. 2021 to Sept. 2021 in Video Group at Megvii Research of Megvii (Beijing, China). In my undergraduate study, I was an team member of computer vision group and mechanics group in Harbin Institute of Technology Competition Robotics Team (HITCRT) (Harbin, China).

Email / Google Scholar / Github / CV

Pinned

✉️ I'm expected to graduate in 2025 and am open to both academia and industry positions. If you're interested, please feel free to contact me.

📌 I'm also seeking self-motivated undergraduate and graduate students for academic collaboration.

News

2024.08: A paper is conditionally accepted to T-RO.

2024.06: Three papers are accepted to IROS 2024, all selected as Oral.

2024.06: Invited talk at TechBeat on the field of Aerial Coverage and Reconstruction.

2024.05: A paper is selected as the finalist for ICRA 2024 Best UAV Paper Award.

2024.02: A paper is accepted to RA-L.

2024.01: Three papers are accepted to ICRA 2024.

2023.12: I have passed Ph.D. Qualifying Examination to become a Ph.D. candidate.

2023.08: Two papers are accepted to RA-L.

2023.01: A paper is accepted to ICRA 2023.

2022.06: A paper is accepted to CVPR 2022 Workshop.

Education

Ph.D. Candidate | Electronic and Computer Engineering (ECE), HKUST
Time: 2021 - Present. Supervisor: Prof. Shaojie Shen

B.Eng | Mechatronics Engineering (ME), HIT
Time: 2017 - 2021.

Research

My research focuses on robotics and deep learning, including perception, prediction, and planning. Before HKUST, my research worked on perception and trajectory prediction in autonomous driving. In the present stage, I focus on autonomous and intelligent perception-centric flight including aerial inspection/reconstruction/exploration/coverage, and 3D scene understanding.

Publications

(^# for the corresponding author, * for equal contribution)

	FALCON: Fast Autonomous Aerial Exploration using Coverage Path Guidance Yichen Zhang, Xinyi Chen, Chen Feng, Boyu Zhou^#, and Shaojie Shen. IEEE Transactions on Robotics (T-RO), 2024. (Conditionally accepted) This paper introduces FALCON, a novel Fast Autonomous expLoration framework using COverage path guidaNce, which aims at setting a new performance benchmark in the field of autonomous aerial exploration. FALCON effectively harnesses the full potential of online generated coverage paths in enhancing exploration efficiency.
	SOAR: Simultaneous Exploration and Photographing with Heterogeneous UAVs for Fast Autonomous Reconstruction Mingjie Zhang, Chen Feng*, Zengzhi Li, Guiyong Zheng, Yiming Luo, Zhu Wang, Jinni Zhou, Shaojie Shen, and Boyu Zhou^#. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024.* Oral (acceptance rate: 10%) Star We introduce SOAR, a LiDAR-Visual heterogeneous multi-UAV planner that enables simultaneous exploration and photographing for fast autonomous reconstruction of complex scenes.
	OmniNxt: A Fully Open-source and Compact Aerial Robot with Omnidirectional Visual Perception Peize Liu, Chen Feng^#, Yang Xu, Yan Ning, Hao Xu^#, and Shaojie Shen. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024. Oral (acceptance rate: 10%) Star We introduce OmniNxt, the first fully open-source aerial robotics platform with omnidirectional visual perception. OmniNxt boasts a compact size with exceptional computational resources. Thanks to our meticulous development, we endow OmniNxt with an expansive perception range and efficient resource consumption.
	MASSTAR: A Multi-Modal Large-Scale Scene Dataset with a Versatile Toolchain for Surface Prediction and Completion Guiyong Zheng, Jinqi Jiang, Chen Feng, Shaojie Shen, and Boyu Zhou^#. Tech Report, 2024.* Star We propose MASSTAR, a Multi-modal lArge-scale Scene dataset with a verSatile Toolchain for surfAce pRediction and completion. We develop a versatile and efficient toolchain for processing the raw 3D data from the environments.
	Star-Searcher: A Complete and Efficient Aerial System for Autonomous Target Search in Complex Unknown Environments Yiming Luo, Zixuan Zhuang, Neng Pan, Chen Feng, Shaojie Shen, Fei Gao, Hui Cheng, and Boyu Zhou^#. IEEE Robotics and Automation Letters (RA-L) with IROS, 2024. Star We propose Star-Searcher, a complete and efficient aerial system for autonomous target search in complex unknown environments. Our aerial system incorporates specialized sensor suites, mapping, and planning modules, all geared towards improving task efficiency and completeness.
	FC-Planner: A Skeleton-guided Planning Framework for Fast Aerial Coverage of Complex 3D Scenes Chen Feng, Haojia Li, Mingjie Zhang, Xinyi Chen, Boyu Zhou^#, and Shaojie Shen. IEEE International Conference on Robotics and Automation (ICRA), 2024. Best UAV Paper Award Finalist Star We propose FC-Planner, a skeleton-guided planning framework tailored for fast coverage of large and complex 3D scenes, which results in the generation of high-quality coverage paths and high computational efficiency.
	MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction Chen Feng, Hangning Zhou, Huadong Lin, Zhigang Zhang, Ziyao Xu, Chi Zhang, Boyu Zhou^#, and Shaojie Shen. IEEE Robotics and Automation Letters (RA-L) with ICRA, 2023. We propose MacFormer, an one-stage Map-Agent Coupled Transformer for real-time and robust trajectory prediction that explicitly incorporates map constraints into the network achieving state-of-the-art performance with significantly lower inference latency and fewer parameters.
	AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Paylaod Transportation Haojia Li, Haokun Wang, Chen Feng, Fei Gao^#, Boyu Zhou^#, and Shaojie Shen. IEEE Robotics and Automation Letters (RA-L) with ICRA, 2023. Star We propose AutoTrans, a systematic solution for fully autonomous aerial payload transportation that includes a real-time planning solution to generate smooth trajectories and an adaptive NMPC with a hierarchical disturbance compensation strategy to overcome unknown external perturbations as well as inaccurate model parameters.
	PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction Chen Feng, Haojia Li, Fei Gao, Boyu Zhou^#, and Shaojie Shen. IEEE International Conference on Robotics and Automation (ICRA), 2023. Star We propose PredRecon, a prediction-boosted planning framework that can efficiently reconstruct high-quality 3D models for the target areas in unknown environments with a single flight.
	TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction Yuting Wang, Hangning Zhou^,#, Zhigang Zhang, Chen Feng, Huadong Lin, Chaofei Gao, Yizhi Tang, Zhenting Zhao, Shiyu Zhang, Jie Guo, Xuefeng Wang, Ziyao Xu, and Chi Zhang. IEEE/CVF Computer Vision and Pattern Recognition Conference Workshop on Autonomous Driving (CVPRW), 2022.* We propose TENET to enhance the trajectory temporal encoding via Temporal Flow Header. Besides, an efficient K-means ensemble method is used. Using our Transformer network and ensemble method, we win the first place of Argoverse 2 Motion Forecasting Challenge with the state-of-the-art brier-minFDE score of 1.90.