(# for the corresponding author, * for equal contributions)
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FC-Planner: A Skeleton-guided Planning Framework for Fast Aerial Coverage of Complex 3D Scenes
Chen Feng, Haojia Li, Jinqi Jiang, Xinyi Chen, Shaojie Shen, and Boyu Zhou#.
[Under Review] Submitted to IEEE International Conference on Robotics and Automation (ICRA),
2024. Yokohama, Japan.
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code /
video
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.
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MASSTAR: A Multi-Modal Large-Scale Scene Dataset with a Versatile Toolchain for Surface Prediction and Completion
Jinqi Jiang*, Guiyong Zheng*, Chen Feng*, Shaojie Shen, and Boyu Zhou#.
[Under Review] Submitted to IEEE International Conference on Robotics and Automation (ICRA),
2024. Yokohama, Japan.
video
We propose MASSTAR, a multi-modal large-scale scene dataset composed of
over a thousand collected scene-level 3D data, which could be used for training and testing different learning methods.
Additionally, a versatile and highly automatic toolchain is developed to generate a multi-modal dataset only from 3D model sets.
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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), 2023. Presented at ICRA 2024, Yokohama, Japan.
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video
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.
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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), 2023. Presented at ICRA 2024, Yokohama, Japan.
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video
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.
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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. London, UK.
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code /
video /
poster
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.
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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. New Orleans, USA.
paper
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.
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