具有自动锚定校准和地形感知融合的工业地面机器人的可部署超宽带定位
Deployment-Ready UWB Localization for Industrial Ground Robots with Automatic Anchor Calibration and Terrain-Aware Fusion
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中文总结 AI 辅助
研究工业地面机器人UWB定位问题，提出结合自动锚定校准与通用多传感器估计器的端到端管道，经实验验证该方法能在减少人工的同时保持定位精度，且可转移到其他平台，还公开了仓库数据集。
中文摘要 AI 辅助
超宽带（UWB）测距由于精度提高和成本低，已成为工业自主移动机器人（AMR）定位的可行选择。然而，实际部署仍受到两个反复出现的挑战的限制：校准静态锚可能既耗时又容易出错，并且将UWB与现有的车载传感器集成需要精心设计，以确保可靠和一致的姿态估计。针对这些挑战，本文提出了一种端到端的管道，该管道将自动锚定校准与针对地面车辆运动定制的通用多传感器估计器相结合。它针对在机器人姿态先验可用于初始化的场景中的现有AMR堆栈。校准阶段估计锚定位置和距离偏差，而定位阶段在偏差感知扩展卡尔曼滤波器中将UWB与本体感知融合，以提高一致性而无需进行广泛的参数调整。在仓库环境中的商业物流AMR上进行的实验表明，在室内和室外过渡中都能进行精确的定位，与早期的估计器公式相比，一致性有所提高。对独立叉车数据集的评估进一步表明该方法可转移到其他平台。该方法在视线有限和锚定覆盖稀疏的测试案例中仍然有效。这些结果表明，UWB定位可以在大大减少人工工作量的情况下进行部署，同时保持工业AMR所需的精度。收集的仓库数据集已公开提供。
英文摘要
Ultra-Wideband (UWB) ranging has become a viable option for industrial Autonomous Mobile Robot (AMR) localization due to improved accuracy and low cost. However, real-world deployments remain limited by two recurring challenges: calibrating static anchors can be time-consuming and error-prone, and integrating UWB with existing onboard sensors requires careful design to ensure robust and consistent pose estimation. Addressing these challenges, this paper presents an end-to-end pipeline that combines automatic anchor calibration with a generic multi-sensor estimator tailored to surface-bound vehicle motion. It targets existing AMR stacks in scenarios where robot pose priors are available for initialization. The calibration stage estimates anchor positions and range biases, while the localization stage fuses UWB with proprioceptive sensing in a bias-aware Extended Kalman Filter to improve consistency without extensive parameter tuning. Experiments on a commercial logistics AMR in a warehouse setting demonstrate accurate positioning indoors and across outdoor transitions, with improved consistency compared to an earlier estimator formulation. Evaluation on an independent forklift dataset further indicates transferability to other platforms. The method remains effective in test cases with limited line-of-sight and sparse anchor coverage. These results show that UWB localization can be deployed with substantially reduced manual effort while preserving the accuracy required for industrial AMRs. The collected warehouse dataset is made publicly available.
发表机构
- AGILOX Services GmbH（阿吉洛克斯服务有限公司）
- University of Klagenfurt（克拉根福大学）
机构由 AI 辅助整理，请以论文原文为准。
