3D object recognition using multiple features for robotic manipulation

TL;DR: A novel 3D object recognition and pose estimation approach based on combining photometric feature (SIFT) and geometric feature (3D lines) in a sequence of scenes using the particle filtering method to increase the certainty by using consecutive scenes.

Abstract: For robust 3D object recognition in the environment having diverse variances, it is necessary to increase the certainty by using consecutive scenes rather than using a single scene and combining different features. This paper proposes a novel 3D object recognition and pose estimation approach based on combining photometric feature (SIFT) and geometric feature (3D lines) in a sequence of scenes. In order to utilize the consecutive scenes, we use the particle filtering method and all particles which represent the possible pose of object are generated by each feature. These particles are to be spread out where the object is considered to exist, and the probability of each particle is obtained through matching test with each feature in the scene. Then the particle sets derived from SIFT and 3D lines are fused and it gives a pose of the object estimated. For the sake of computational efficiency, this recognition system is performed in a hierarchical process. In this paper, we also introduce a simple method to decide the next best view position based on results of recognition. Lastly we have proved through experiments that the proposed methods are feasible