Uncertainty estimation of AR-marker poses for graph-SLAM optimization in 3D object model generation with RGBD data

This paper presents an approach to acquire textured 3D models of objects without the need for sophisticated hardware infrastructures. The approach is inexpensive, using a low-cost Microsoft Kinect RGB-D sensor and Augmented Reality (AR) markers printed on paper sheets. The AR-markers can be freely placed in the scene, allowing the modeling of objects of various sizes, and the sensor can be moved by the hand of an untrained person. To generate usable models with this very inexpensive and simple setup, the sequence of RGB-D scans is embedded in a graph-based optimizer for automatic post-refinement. The main novelty of this contribution is the development of an uncertainty model for an AR-marker. The AR-marker uncertainty models are used as constraints in an optimization problem to better estimate the object pose. The models are in the end further fine-tuned by a standard point-based registration algorithm. The results section presents realistic models of various objects generated using this system, e.g., parcels, sport balls, human dolls etc. Additionally, a quantitative analysis is presented using objects of known dimensions.