Extrinsic Calibration of Multiple Inertial Sensors from Arbitrary Trajectories

TL;DR

This paper introduces a novel method for extrinsic calibration of multiple IMUs that relies solely on their measurements, eliminating the need for predefined trajectories or additional sensors like cameras.

Contribution

It proposes a nonlinear least-squares approach for IMU-only calibration that is robust, efficient, and trajectory-insensitive, outperforming some existing methods requiring extra hardware.

Findings

Achieves comparable or better accuracy than camera-based methods.

Demonstrates robustness across various trajectories in simulations and hardware.

Offers fast computation suitable for real-time applications.

Abstract

We present a method of extrinsic calibration for a system of multiple inertial measurement units (IMUs) that estimates the relative pose of each IMU on a rigid body using only measurements from the IMUs themselves, without the need to prescribe the trajectory. Our method is based on solving a nonlinear least-squares problem that penalizes inconsistency between measurements from pairs of IMUs. We validate our method with experiments both in simulation and in hardware. In particular, we show that it meets or exceeds the performance -- in terms of error, success rate, and computation time -- of an existing, state-of-the-art method that does not rely only on IMU measurements and instead requires the use of a camera and a fiducial marker. We also show that the performance of our method is largely insensitive to the choice of trajectory along which IMU measurements are collected.