发表机构
The University of Alabama（阿拉巴马大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出DARP双臂机器人感知数据集，含10种桌面物体，经评估几何一致性良好，可用于多视角重建等多类机器人感知相关研究。
AI 中文摘要
单一视角的机器人感知常受自遮挡和表面可见性不完整的限制。本文提出DARP（Dual-Arm Robotic Perception，双臂机器人感知），这是一个用于以物体为中心的机器人感知的校准双臂RGB-D-IR数据集，使用两个独立移动的眼在手上式机械臂，放置在共享桌面工作区的相对两侧。每个机械臂搭载一个Intel RealSense传感器，持续记录RGB、深度和立体红外数据，同时记录同步的机器人关节状态以用于位姿恢复。物体放置时无固定位姿或标记位置，采集过程执行自动定位、跨臂确认、自适应视角生成和连续多模态记录。DARP包含10种独特的桌面物体，并保留原始传感器记录、机器人状态日志、物体级元数据以及在共享度量坐标系中重建相机轨迹所需的校准信息。为评估采集的几何一致性，我们实现了确定性多视角融合流水线，将校准后的RGB-D观测转换为互补的部分点云和测量表面网格，不使用基于学习或生成式补全方法。对包含1563466个三维查询点的224个预留RGB-D关键帧进行评估，得到中位数点到网格距离为2.13mm，均方根误差（RMSE）为4.04mm，96.56%的点位于测量表面网格的10mm范围内。DARP旨在作为可复用资源，用于多视角重建、协作机器人感知、多模态融合、主动感知以及对部分物体观测的未来基于学习的推理。
英文摘要
Robotic perception from a single viewpoint is often limited by self-occlusion and incomplete surface visibility. This paper presents DARP(Dual-Arm Robotic Perception) https://doi.org/10.21227/rmv3-be47, a calibrated dual-arm RGB-D-IR dataset for object-centered robotic perception using two independently moving eye-in-hand manipulators positioned on opposite sides of a shared tabletop workspace. Each arm carries an Intel RealSense sensor that continuously records RGB, depth, and stereo infrared data while synchronized robot joint states are logged for pose recovery. Objects are placed without fixed poses or marked locations, and the acquisition procedure performs automatic localization, cross-arm confirmation, adaptive viewpoint generation, and continuous multimodal recording. DARP contains ten unique tabletop objects and preserves the original sensor recordings, robot-state logs, object-level metadata, and calibration information required to reconstruct camera trajectories in a shared metric frame. To evaluate the geometric consistency of the acquisition, we implement a deterministic multi-view fusion pipeline that converts calibrated RGB-D observations into complementary partial point clouds and measured surface meshes without using learned or generative completion methods. Evaluation on 224 held-out RGB-D keyframes comprising 1,563,466 three-dimensional query points yields a median point-to-mesh distance of 2.13~mm and an RMSE of 4.04~mm, with 96.56\% of points within 10~mm of the measured-surface mesh. DARP is intended as a reusable resource for multi-view reconstruction, collaborative robotic perception, multimodal fusion, active perception, and future learning-based reasoning over partial object observations.
