无需调平或旋转，就能精准校准低成本加速度计的零偏误差。
Orientation-Free Neural Network-Based Bias Estimation for Low-Cost Stationary Accelerometers
- 基于无模型学习的方法，在静止状态下估计零偏，不依赖传感器朝向。
- 实测13.39小时数据表明，误差比传统方法低52%以上。
- 适合野外快速部署，提升低精度惯性传感器在各领域的可靠性。
低成本微机电加速度计广泛用于导航、机器人和消费设备中的运动感知与位置估计，但其性能常受零偏误差影响。传统校准需在静止条件下进行水平放置或复杂的姿态相关操作。本文提出一种无需姿态信息、无需旋转传感器的模型无关学习校准方法，可在静止状态直接估计零偏。在六台加速度计采集的13.39小时数据上验证，该方法误差显著低于传统技术，平均降低超52%。本工作推动了无方向依赖校准的发展，提升了低成本惯性传感器在科研与工业应用中的可靠性，免除了水平校准的繁琐要求。
原文摘要 · Abstract (English)
Low-cost micro-electromechanical accelerometers are widely used in navigation, robotics, and consumer devices for motion sensing and position estimation. However, their performance is often degraded by bias errors. To eliminate deterministic bias terms a calibration procedure is applied under stationary conditions. It requires accelerom- eter leveling or complex orientation-dependent calibration procedures. To overcome those requirements, in this paper we present a model-free learning-based calibration method that estimates accelerometer bias under stationary conditions, without requiring knowledge of the sensor orientation and without the need to rotate the sensors. The proposed approach provides a fast, practical, and scalable solution suitable for rapid field deployment. Experimental validation on a 13.39-hour dataset collected from six accelerometers shows that the proposed method consistently achieves error levels more than 52% lower than traditional techniques. On a broader scale, this work contributes to the advancement of accurate calibration methods in orientation-free scenarios. As a consequence, it improves the reliability of low-cost inertial sensors in diverse scientific and industrial applications and eliminates the need for leveled calibration.
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