Pickalo：利用6D位姿估计实现低成本工业装箱拾取
Pickalo: Leveraging 6D Pose Estimation for Low-Cost Industrial Bin Picking
AI总结：
Pickalo通过低成本硬件实现6D位姿估计，结合深度估计和姿态缓冲模块，在工业环境中实现高效率的装箱拾取，达到每小时600次拾取，成功率达96-99%。
摘要：
现实工业环境中的装箱拾取仍面临严重杂乱、遮挡和传统3D传感成本高的挑战。我们提出Pickalo，一个完全基于低成本硬件的模块化6D位姿拾取流水线。腕部安装的RGB-D相机从多个视角主动探索场景，原始立体流通过BridgeDepth处理获得精确的深度图，用于准确的碰撞推理。对象实例通过训练纯在真实感合成数据上的Mask-RCNN模型进行分割，并利用零样本SAM-6D位姿估计器进行定位。姿态缓冲模块融合多视角观察，处理对象对称性和显著减少位姿噪声。离线生成并整理每个对象的反向抓取候选集；在线通过基于效用的排名和快速碰撞检查进行抓取规划。在UR5e配合平行齿抓取手和Intel RealSense D435i上部署，Pickalo实现每小时600次平均拾取，成功率达96-99%，在密集装箱环境中表现稳定。消融研究证明了深度估计增强和姿态缓冲模块在真实工业条件下的长期稳定性和吞吐量优势。
英文摘要：
Bin picking in real industrial environments remains challenging due to severe clutter, occlusions, and the high cost of traditional 3D sensing setups. We present Pickalo, a modular 6D pose-based bin-picking pipeline built entirely on low-cost hardware. A wrist-mounted RGB-D camera actively explores the scene from multiple viewpoints, while raw stereo streams are processed with BridgeDepth to obtain refined depth maps suitable for accurate collision reasoning. Object instances are segmented with a Mask-RCNN model trained purely on photorealistic synthetic data and localized using the zero-shot SAM-6D pose estimator. A pose buffer module fuses multi-view observations over time, handling object symmetries and significantly reducing pose noise. Offline, we generate and curate large sets of antipodal grasp candidates per object; online, a utility-based ranking and fast collision checking are queried for the grasp planning. Deployed on a UR5e with a parallel-jaw gripper and an Intel RealSense D435i, Pickalo achieves up to 600 mean picks per hour with 96-99% grasp success and robust performance over 30-minute runs on densely filled euroboxes. Ablation studies demonstrate the benefits of enhanced depth estimation and of the pose buffer for long-term stability and throughput in realistic industrial conditions. Videos are available at https://mesh-iit.github.io/project-jl2-camozzi/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
