RGB-D camera-based parallel tracking and meshing

Abstract

Compared to standard color cameras, RGB-D cameras are designed to additionally provide the depth of imaged pixels which in turn results in a dense colored 3D point cloud representing the environment from a certain viewpoint. We present a real-time tracking method that performs motion estimation of a consumer RGB-D camera with respect to an unknown environment while at the same time reconstructing this environment as a dense textured mesh. Unlike parallel tracking and mapping performed with a standard color or grey scale camera, tracking with an RGB-D camera allows a correctly scaled camera motion estimation. Therefore, there is no need for measuring the environment by any additional tool or equipping the environment by placing objects in it with known sizes. The tracking can be directly started and does not require any preliminary known and/or constrained camera motion. The colored point clouds obtained from every RGB-D image are used to create textured meshes representing the environment from a certain camera view and the real-time estimated camera motion is used to correctly align these meshes over time in order to combine them into a dense reconstruction of the environment. We quantitatively evaluated the proposed method using real image sequences of a challenging scenario and their corresponding ground truth motion obtained with a mechanical measurement arm. We also compared it to a commonly used state-of-the-art method where only the color information is used. We show the superiority of the proposed tracking in terms of accuracy, robustness and usability. We also demonstrate its usage in several Augmented Reality scenarios where the tracking allows a reliable camera motion estimation and the meshing increases the realism of the augmentations by correctly handling their occlusions.