Orientation-Free Neural Network-Based Bias Estimation for Low-Cost Stationary Accelerometers

TL;DR

This paper introduces a novel, model-free calibration method for low-cost stationary accelerometers that estimates bias without orientation knowledge, significantly improving accuracy and simplifying deployment.

Contribution

It presents an orientation-free, learning-based calibration approach that eliminates the need for sensor leveling or rotation, enhancing practicality and scalability.

Findings

Achieves over 52% lower error than traditional calibration methods.

Validates effectiveness on a 13.39-hour dataset from six sensors.

Provides a fast, scalable solution for real-world applications.

Abstract

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.