Inertial-Based Scale Estimation for Structure from Motion on Mobile Devices
This solves the scale estimation problem for mobile device-based 3D reconstruction, enabling more accurate visual reconstructions without parameter tuning, though it is incremental as it builds on existing IMU-camera fusion approaches.
The paper tackles the inherent scale ambiguity in structure from motion by using inertial measurements from mobile device IMUs to recover metric scale, achieving around 1% accuracy from ground truth and outperforming state-of-the-art methods in accuracy and convergence speed.
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.