NanoMVG: USV-Centric Low-Power Multi-Task Visual Grounding based on Prompt-Guided Camera and 4D mmWave Radar
Runwei Guan, Jianan Liu, Liye Jia, Haocheng Zhao, Shanliang Yao, Xiaohui Zhu, Ka Lok Man, Eng Gee Lim
Abstract
Recently, visual grounding and multi-sensors setting have been incorporated into perception system for terrestrial autonomous driving systems and Unmanned Surface Vessels (USVs), yet the high complexity of modern learning-based visual grounding model using multi-sensors prevents such model to be deployed on USVs in the real-life. To this end, we design a low-power multi-task model named NanoMVG for waterway embodied perception, guiding both camera and 4D millimeter-wave radar to locate specific object(s) through natural language. NanoMVG can perform both box-level and mask-level visual grounding tasks simultaneously. Compared to other visual grounding models, NanoMVG achieves highly competitive performance on the WaterVG dataset, particularly in harsh environments. Moreover, the real-world experiments with deployment of NanoMVG on embedded edge device of USV demonstrates its fast inference speed for real-time perception and capability of boasting ultra-low power consumption for long endurance.
BibTeX
@inproceedings{iros2025_nanomvgusvcentri,
title = {NanoMVG: USV-Centric Low-Power Multi-Task Visual Grounding based on Prompt-Guided Camera and 4D mmWave Radar},
author = {Runwei Guan and Jianan Liu and Liye Jia and Haocheng Zhao and Shanliang Yao and Xiaohui Zhu and Ka Lok Man and Eng Gee Lim and Jeremy S. Smith and Yutao Yue},
booktitle = {IROS 2025},
year = {2025}
}