Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline
This work addresses incremental improvements in 3D reconstruction for computer vision applications, specifically enhancing the KinectFusion pipeline with IMU data.
The paper tackles the problem of improving 3D reconstruction in the KinectFusion pipeline by integrating absolute orientation measurements from low-cost IMU sensors, resulting in a 12% speed-up, 53% gain in tracking quality on the Freiburg benchmark, and enhanced robustness.
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.