为移动机器人设计的6D位姿估计算法评测数据集
MR6D: Benchmarking 6D Pose Estimation for Mobile Robots
- 构建工业场景下移动机器人的6D位姿数据集
- 包含92个真实场景与16类大尺寸物体，覆盖远距离视角与复杂遮挡
- 适合研究移动机器人位姿估计的学者和工程师
现有6D位姿估计算法多针对小型家用物体，主要服务于机械臂操作，难以适配移动机器人。移动平台通常无机械臂、需处理大型物体，面临远距离感知、严重自遮挡和多样相机视角等挑战。尽管近年模型对未见物体具备良好泛化能力，但评估仍局限于类家庭环境，忽略上述关键因素。本文提出MR6D数据集，专为移动机器人在工业环境中的6D位姿估计设计。数据集包含92个真实场景，涵盖16种独特物体，涉及静态与动态交互。其特点包括远距离视角、多变物体配置、较大物体尺寸及复杂遮挡/自遮挡模式。初步实验表明，当前6D方法在此场景下性能显著下降，2D分割仍是主要瓶颈。MR6D为开发和评估面向移动机器人需求的位姿估计方法奠定基础。数据集已公开于https://huggingface.co/datasets/anas-gouda/mr6d。
原文摘要 · Abstract (English)
Existing 6D pose estimation datasets primarily focus on small household objects typically handled by robot arm manipulators, limiting their relevance to mobile robotics. Mobile platforms often operate without manipulators, interact with larger objects, and face challenges such as long-range perception, heavy self-occlusion, and diverse camera perspectives. While recent models generalize well to unseen objects, evaluations remain confined to household-like settings that overlook these factors. We introduce MR6D, a dataset designed for 6D pose estimation for mobile robots in industrial environments. It includes 92 real-world scenes featuring 16 unique objects across static and dynamic interactions. MR6D captures the challenges specific to mobile platforms, including distant viewpoints, varied object configurations, larger object sizes, and complex occlusion/self-occlusion patterns. Initial experiments reveal that current 6D pipelines underperform in these settings, with 2D segmentation being another hurdle. MR6D establishes a foundation for developing and evaluating pose estimation methods tailored to the demands of mobile robotics. The dataset is available at https://huggingface.co/datasets/anas-gouda/mr6d.
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