Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline

TL;DR

This paper integrates IMU-based absolute orientation measurements into the KinectFusion pipeline, enhancing 3D reconstruction quality, robustness, and speed through improved registration and filtering techniques.

Contribution

It introduces a novel method to incorporate IMU data into KinectFusion, including GPU-accelerated filtering and improved ICP registration, leading to better performance.

Findings

12% speed-up in reconstruction

53% improvement in tracking accuracy

Enhanced robustness of 3D reconstruction

Abstract

In this paper, we show how absolute orientation measurements provided by low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion pipeline. We show that integration improves both runtime, robustness and quality of the 3D reconstruction. In particular, we use this orientation data to seed and regularize the ICP registration technique. We also present a technique to filter the pairs of 3D matched points based on the distribution of their distances. This filter is implemented efficiently on the GPU. Estimating the distribution of the distances helps control the number of iterations necessary for the convergence of the ICP algorithm. Finally, we show experimental results that highlight improvements in robustness, a speed-up of almost 12%, and a gain in tracking quality of 53% for the ATE metric on the Freiburg benchmark.