Inertial-Based Scale Estimation for Structure from Motion on Mobile Devices

TL;DR

This paper introduces a robust, parameter-free method for estimating the metric scale in structure from motion using inertial measurements from mobile devices, improving accuracy and convergence speed.

Contribution

It presents a novel frequency domain approach for scale estimation that handles noisy data and aligns camera and IMU measurements without parameter tuning.

Findings

Outperforms state-of-the-art in accuracy and speed

Achieves around 1% scale accuracy from ground truth

Enhances Project Tango's motion tracking precision

Abstract

Structure from motion algorithms have an inherent limitation that the reconstruction can only be determined up to the unknown scale factor. Modern mobile devices are equipped with an inertial measurement unit (IMU), which can be used for estimating the scale of the reconstruction. We propose a method that recovers the metric scale given inertial measurements and camera poses. In the process, we also perform a temporal and spatial alignment of the camera and the IMU. Therefore, our solution can be easily combined with any existing visual reconstruction software. The method can cope with noisy camera pose estimates, typically caused by motion blur or rolling shutter artifacts, via utilizing a Rauch-Tung-Striebel (RTS) smoother. Furthermore, the scale estimation is performed in the frequency domain, which provides more robustness to inaccurate sensor time stamps and noisy IMU samples than the previously used time domain representation. In contrast to previous methods, our approach has no parameters that need to be tuned for achieving a good performance. In the experiments, we show that the algorithm outperforms the state-of-the-art in both accuracy and convergence speed of the scale estimate. The accuracy of the scale is around $1\%$ from the ground truth depending on the recording. We also demonstrate that our method can improve the scale accuracy of the Project Tango's build-in motion tracking.