用低成本设备实现高精度工业抓取，支持复杂场景下持续稳定作业
Pickalo: Leveraging 6D Pose Estimation for Low-Cost Industrial Bin Picking
- 基于腕装摄像头多视角采集，结合深度优化与6D姿态估计定位物体
- 每小时可完成600次抓取，成功率96%-99%，30分钟运行无故障
- 适合工业自动化场景，尤其对成本敏感且需长期稳定运行的项目
真实工业环境中的物料抓取仍面临严重遮挡、杂乱及传统3D传感设备成本高等挑战。本文提出Pickalo，一种完全基于低成本硬件的6D姿态驱动式抓取流程。腕装RGB-D相机从多角度主动探测场景，原始双目流通过BridgeDepth生成适合精确碰撞推理的优化深度图。物体实例采用仅在照片级合成数据上训练的Mask-RCNN模型分割，并使用零样本SAM-6D姿态估计算法定位。姿态缓冲模块融合多视角观测，有效处理物体对称性并显著降低姿态噪声。离线阶段为每类物体生成并筛选大量对偶抓取候选；在线阶段通过效用评分与快速碰撞检测进行抓取规划。系统部署于UR5e机械臂与Intel RealSense D435i相机，实测在密集填充的eurobox中实现每小时600次抓取，成功率96%-99%，连续30分钟运行表现稳健。消融实验验证了增强深度估计与姿态缓冲在真实工业条件下对长期稳定性与吞吐量的提升作用。视频见https://mesh-iit.github.io/project-jl2-camozzi/
原文摘要 · Abstract (English)
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/
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