A Probabilistic Fusion Framework for 3-D Reconstruction Using Heterogeneous Sensors

Abstract

This letter proposes a framework to perform 3-D reconstruction using a heterogeneous sensor network, with potential use in augmented reality, human behavior understanding, smart-room implementations, robotics, and many other applications. We fuse orientation measurements from inertial sensors, images from cameras and depth data from Time of Flight sensors within a probabilistic framework in a synergistic manner to obtain robust reconstructions. A fully probabilistic method is proposed to efficiently fuse the multi-modal data of the system.