Probabilistic 3D Object Recognition Based on Multiple Interpretations Generation

We present a probabilistic 3D object recognition approach using multiple interpretations generation in cluttered domestic environment. How to handle pose ambiguity and uncertainty is the main challenge in most recognition systems. In our approach, invariant 3D lines are employed to generate the pose hypotheses as multiple interpretations, especially ambiguity from partial occlusion and fragment of 3D lines are taken into account. And the estimated pose is represented as a region instead of a point in pose space by considering the measurement uncertainties. Then, probability of each interpretation is computed reliably using Bayesian principle in terms of both likelihood and unlikelihood. Finally, fusion strategy is applied to a set of top ranked interpretations, which are further verified and refined to make more accurate pose estimation in real time. The experimental results support the potential of the proposed approach in the real cluttered domestic environment.