Ensemble classifier for joint object instance and category recognition on RGB-D data

Sensors for RGB-D data have gained high popularity in the computer vision community. We present an efficient ensemble classifier that combines visual and depth data and achieves higher recognition rates than the individual classifiers or a classifier exploiting visual and depth data at the same time. The presented approach was evaluated in practice on a mobile robot during the RoCKIn robotics challenge in 2014.