publications

2026

1. In Review

   

   UNRIO: Uncertainty-Aware Velocity Learning for Radar-Inertial Odometry

   Jui-Te Huang, Tianshu Huang, Anthony Rowe, and Michael Kaess

   . In review , 2026

   Abs Bib HTML Website

   mmWave radar enables robust ego-velocity estimation in visually denied conditions, but existing methods rely on sparse point clouds prone to limited FOV and Doppler aliasing. UNRIO instead uses raw I/Q signals with a radar foundation model, combining uncertainty-aware velocity estimates with IMU measurements for state estimation. It demonstrates robust performance across diverse scenarios, including lateral motion and severe Doppler aliasing.

   @article{huang2026unrio,
     title = {UNRIO: Uncertainty-Aware Velocity Learning for Radar-Inertial Odometry},
     author = {Huang, Jui-Te and Huang, Tianshu and Rowe, Anthony and Kaess, Michael},
     year = {2026},
   }

2. IROS

   

   Millimeter Wave Radar: From Synthetic Aperture to Probabilistic Mapping

   Jui-Te Huang, Ruoyang Xu, and Michael Kaess

   In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2026

   Abs Bib HTML Code

   We propose a SAR-based pipeline for probabilistic occupancy mapping using mmWave radar in challenging conditions like smoke and fog. Extensive experiments evaluate mapping accuracy, path planning, and system parameters. We also release an open-source radar dataset and GPU-accelerated processing pipeline.

   @inproceedings{huang2026rpm,
     title = {Millimeter Wave Radar: From Synthetic Aperture to Probabilistic Mapping},
     author = {Huang, Jui-Te and Xu, Ruoyang and Kaess, Michael},
     booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
     year = {2026},
   }

2024

1. ICRA

   

   Multi-Radar Inertial Odometry for 3D State Estimation using mmWave Imaging Radar

   Jui-Te Huang, Ruoyang Xu, Akshay Hinduja, and Michael Kaess

   In IEEE International Conference on Robotics and Automation (ICRA), 2024

   Abs arXiv Bib HTML Video

   In this paper, we address Doppler velocity measurement uncertainties. We present a method to optimize body frame velocity while managing Doppler velocity uncertainty. Based on our observations, we propose a dual imaging radar configuration to mitigate the challenge of discrepancy in radar data. To attain high-precision 3D state estimation, we introduce a strategy that seamlessly integrates radar data with a consumer-grade IMU sensor using fixed-lag smoothing optimization. Finally, we evaluate our approach using real-world 3D motion data.

   @inproceedings{huang2024mrio,
     title = {Multi-Radar Inertial Odometry for 3D State Estimation using mmWave Imaging Radar},
     author = {Huang, Jui-Te and Xu, Ruoyang and Hinduja, Akshay and Kaess, Michael},
     booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
     year = {2024},
   }

2023

1. IV

   

   Vision meets mmWave Radar: 3D Object Perception Benchmark for Autonomous Driving

   Yizhou Wang, Jen-Hao Cheng, Jui-Te Huang, Sheng-Yao Kuan, and 7 more authors

   In IEEE Intelligent Vehicles Symposium (IV), 2023

   Abs HTML

   We introduce the CRUW3D dataset, including 66K synchronized and well-calibrated camera, radar, and LiDAR frames in various driving scenarios. Unlike other large-scale autonomous driving datasets, our radar data is in the format of radio frequency (RF) tensors that contain not only 3D location information but also spatio-temporal semantic information.

2022

1. JFR

   

   A Heterogeneous Unmanned Ground Vehicle and Blimp Robot Team for Search and Rescue using Data-driven Autonomy and Communication-aware Navigation

   Chen-Lung Lu, Jui-Te Huang, Ching-I Huang, Zi-Yan Liu, and 12 more authors

   Journal of Field Robotics. DARPA SubT Challenge, Urban Circuit, 2020 , 2022

   Abs Bib HTML Video

   The DARPA Subterranean (SubT) Challenge aims to develop innovative technologies that would augment operations underground. The SubT Challenge will explore new approaches to rapidly map, navigate, search, and exploit complex underground environments. I participated in the Urban Circuit as the software team lead and human supervisor. We built our robots to perform search and rescue missions in an unfinished nuclear power plant.

   @article{lu2022subturban,
     title = {A Heterogeneous Unmanned Ground Vehicle and Blimp Robot Team for Search and Rescue using Data-driven Autonomy and Communication-aware Navigation},
     author = {Lu, Chen-Lung and Huang, Jui-Te and Huang, Ching-I and Liu, Zi-Yan and Hsu, Chao-Chun and Huang, Yu-Yen and Huang, Siao-Cing and Chang, Po-Kai and Ewe, Zu Lin and Huang, Po-Jui and Li, Po-Lin and Wang, Bo-Hui and Yim, Lai-Sum and Huang, Sheng-Wei and Bai, MingSian R. and Wang, Hsueh-Cheng},
     journal = {Journal of Field Robotics},
     year = {2022},
   }

2021

1. RA-L

   

   Cross-Modal Contrastive Learning of Representations for Navigation Using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental Conditions

   Jui-Te Huang, Chen-Lung Lu, Po-Kai Chang, Ching-I Huang, and 3 more authors

   IEEE Robotics and Automation Letters, 2021

   Abs arXiv Bib HTML Video Code

   We propose the use of single-chip millimeter-wave (mmWave) radar, which is lightweight and inexpensive, for learning-based autonomous navigation. However, because mmWave radar signals are often noisy and sparse, we propose a cross-modal contrastive learning for representation (CM-CLR) method that maximizes the agreement between mmWave radar data and LiDAR data in the training stage to enable autonomous navigation using radar signal.

   @article{huang2021mmwave,
     title = {Cross-Modal Contrastive Learning of Representations for Navigation Using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental Conditions},
     author = {Huang, Jui-Te and Lu, Chen-Lung and Chang, Po-Kai and Huang, Ching-I and Hsu, Chao-Chun and Huang, Po-Jui and Wang, Hsueh-Cheng},
     journal = {IEEE Robotics and Automation Letters},
     year = {2021},
   }

2. Front. Robot. AI

   

   Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People

   Chen-Lung Lu, Zi-Yan Liu, Jui-Te Huang, Ching-I Huang, and 6 more authors

   Frontiers in Robotics and AI, 2021

   Abs HTML Website

   Facilitating navigation in pedestrian environments is critical for enabling people who are blind and visually impaired (BVI) to achieve independent mobility. A deep-reinforcement-learning-based assistive guiding robot with ultrawide-bandwidth (UWB) beacons that can navigate through routes with designated waypoints was designed in this study.

2019

1. IROS

   

   Duckiepond: An Open Education and Research Platform for a Fleet of Autonomous Maritime Vehicles

   Ni-Ching Lin, Yu-Chieh Hsiao, Yi-Wei Huang, Ching-Tung Hung, and 8 more authors

   In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Also presented at the Sixth Biennial Meeting of the MOOS Development and Application Working Group, Cambridge MA, 2019 , 2019

   Abs HTML Code Website

   This project is to help people who are interested in marine robotics. We built a low-cost surface vehicle called duckieboat. Our vehicles are compatible with machine learning frameworks. We demonstrate the implementation of classic autonomous navigation algorithms and tracking vehicles using deep learning computer vision and sensors on board.