# Derivation-Based Calibration of IMUs Using Savitzky–Golay Filters

## TL;DR

This paper introduces a new IMU calibration method for robots that avoids integration errors by using derivatives and Savitzky–Golay filters.

## Contribution

A novel derivative-based IMU calibration method using Savitzky–Golay filters to reduce integration errors.

## Key findings

- The proposed method achieves higher calibration accuracy than existing methods.
- It is effective for industrial-grade IMUs and validated using simulated ground truth data.

## Abstract

For any robotic application, accurate sensor calibration is crucial. In the case of mobile platforms or flying drones, a sensor commonly utilized is the Inertial Measurement Unit (IMU). Current approaches to the calibration of IMU-equipped robotic systems focus on sensor-to-sensor calibration, meaning a second sensor is necessary for the calibration process. Furthermore, a great number of those rely on integrating the sensor measurements to obtain its pose, which leads to integration errors. In this work, we present a method for the extrinsic calibration of IMUs in robotic systems, which avoids the errors originating from IMU integration by instead taking a derivative approach using Savitzky–Golay filters. The proposed calibration method estimates the transformation between an IMU sensor and its parent frame in the system’s kinematic chain by minimizing the differences between derived linear accelerations and angular velocities and those measured by the sensor. Simulated data is used to establish a ground truth against which the calibration results are compared. Results indicate that the proposed method achieves a higher accuracy than the alternatives it is compared against, while also showing the method can be applied to industrial-grade IMUs.