Multimodal cue integration through Hypotheses Verification for RGB D object recognition and 6DOF pose estimation
This paper proposes an effective algorithm for recognizing objects and accurately estimating their 6DOF pose in scenes acquired by a RGB D sensor. The proposed method is based on a combination of different recognition pipelines, each exploiting the data in a diverse manner and generating object hypotheses that are ultimately fused together in an Hypothesis Verification stage that globally enforces geometrical consistency between model hypotheses and the scene. Such a scheme boosts the overall recognition performance as it enhances the strength of the different recognition pipelines while diminishing the impact of their specific weaknesses. The proposed method outperforms the state of the art on two challenging benchmark datasets for object recognition comprising 35 object models and, respectively, 176 and 353 scenes.
2013 IEEE International Conference on Robotics and Automation