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

arXiv: https://arxiv.org/abs/2511.13071 · HTML: https://mlsanity.com/paper/2511.13071/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Understanding Batch Normalization](https://mlsanity.com/paper/1806.02375/) (2018)
- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions](https://mlsanity.com/paper/2307.00014/) (2023)
- [A Learning-based approach for bias elimination in low cost gyroscopes](https://mlsanity.com/paper/2210.04568/) (2022)
- [Rapid Gyroscope Calibration: A Deep Learning Approach](https://mlsanity.com/paper/2409.00488/) (2024)
- [JAXFit: Trust Region Method for Nonlinear Least-Squares Curve Fitting on the GPU](https://mlsanity.com/paper/2208.12187/) (2022)

## Abstract (arXiv)

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.