发表机构
Southern University of Science and Technology; Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学; 南方科技大学嘉兴研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对机器人在杂乱动态环境中定位行李手推车的问题，提出统一多任务协作感知网络UMCP。基于YOLOv12架构，融合特征并采用特定建模提升方向估计精度，实验证明该方法能在降低成本的同时保持竞争力。
AI 中文摘要
在机器人自主行李手推车收集任务中，机器人需在杂乱动态环境中持续定位分散的行李手推车，这要求视觉系统兼具高精度与实时性。现有方法常依赖级联多模型推理，导致推理延迟增加和部署成本高。本文提出统一多任务协作感知网络（UMCP），它能同时进行行李手推车检测、关键点检测和方向估计。基于YOLOv12架构，融合关键点特征与方向特征并送入方向特征增强模块，还采用带KL散度损失的圆形概率分布建模进一步提升方向估计精度。实验表明该方法在降低模型复杂度和计算成本的同时，总体精度具有竞争力。
英文摘要
In robotic autonomous luggage trolley collection, robots must continuously localize scattered luggage trolleys in cluttered and dynamic environments. This requires the vision system to achieve both high accuracy and real-time performance. However, existing visual perception approaches for luggage trolleys often rely on cascaded multi-model inference, leading to increased inference latency and high deployment costs. To address these limitations, this article presents a unified multi-task collaborative perception network (UMCP) that simultaneously performs luggage trolley detection, keypoint detection and orientation estimation. Based on the YOLOv12 architecture, keypoint features are fused with orientation features and then fed into an orientation feature enhancement module (OFEM), thereby improving orientation estimation accuracy. In addition, circular probability distribution modeling with a Kullback-Leibler (KL) divergence loss is adopted to enhance orientation estimation accuracy further. Experimental results demonstrate that the proposed method achieves competitive overall accuracy while substantially reducing model complexity and computational cost compared with existing methods. A website about this work is available at https://sites.google.com/view/robot-umcp.
