用高精度加速度计+因子图模型，提升建筑机器人定位精度
Enhancing Robotic Precision in Construction: A Modular Factor Graph-Based Framework to Deflection and Backlash Compensation Using High-Accuracy Accelerometers
- 基于因子图的模块化框架，融合加速度数据实时估计机械臂状态
- 在xy平面将95%误差阈值降低50%，加基座倾斜补偿后降31%
- 适合需高精度定位的建筑自动化场景，尤其长臂机器人
精准定位在建筑行业至关重要，劳动力短缺促使自动化需求上升。长机械链机器人需抵达复杂工作空间（如地板、墙面、天花板），但链中各部件的变形与间隙会显著影响定位精度。本文提出一种新方法，将变形与间隙补偿模型与高精度加速度计结合，显著提升定位精度。采用基于因子图的模块化框架，利用加速度测量信息推断机械链状态。在公开数据集上的大量测试表明，该方法相比当前最优的虚拟关节法，在xy平面将95%误差阈值降低50%，加入基座倾斜补偿后进一步降低31%。
原文摘要 · Abstract (English)
Accurate positioning is crucial in the construction industry, where labor shortages highlight the need for automation. Robotic systems with long kinematic chains are required to reach complex workspaces, including floors, walls, and ceilings. These requirements significantly impact positioning accuracy due to effects such as deflection and backlash in various parts along the kinematic chain. In this work, we introduce a novel approach that integrates deflection and backlash compensation models with high-accuracy accelerometers, significantly enhancing position accuracy. Our method employs a modular framework based on a factor graph formulation to estimate the state of the kinematic chain, leveraging acceleration measurements to inform the model. Extensive testing on publicly released datasets, reflecting real-world construction disturbances, demonstrates the advantages of our approach. The proposed method reduces the $95\%$ error threshold in the xy-plane by $50\%$ compared to the state-of-the-art Virtual Joint Method, and by $31\%$ when incorporating base tilt compensation.
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