Scene Regions Guided Pose Estimation Using an Improved Voting Method in Cluttered Scenes In the process of service robot performing home services, one of the key parts is robotic grasping. Meanwhile, accurate object pose estimation is essential for grasping. In home environments, estimating the poses of household textureless objects simply and effectively in cluttered and occluded scenes is challenging. This paper proposes a method by using the color information of object to extract the 3D scene regions where the object may exist. Point Pair Features voting approach is applied to obtained voting array in extracted 3D scenes. Then a novel votes adjustment method is proposed to recalculate the voting number to reduce the effects of occlusion. Our algorithm is evaluated on Linemod Occluded dataset and the experimental results show that the proposed algorithm can effectively improve the accuracy of pose estimation when there is object occlusion in a cluttered scene and improve the ranking of correct pose in candidate poses. Meanwhile, the average calculation time is shortened.

ROIbased Robotic Grasp Detection for Object Overlapping Scenes Grasp detection considering the affiliations between grasps and their owner in object overlapping scenes is a necessary and challenging task for the practical use of the robotic grasping approach. In this paper, a robotic grasp detection algorithm named ROIGD is proposed to provide a feasible solution to this problem based on Region of Interest (ROI), which is the region proposal for objects. ROIGD uses features from ROIs to detect grasps instead of the whole scene. It has two stages: the first stage is to provide ROIs in the input image and the secondstage is the grasp detector based on ROI features. We also contribute a multiobject grasp dataset, (a) which is much larger than Cornell Grasp Dataset, by labeling Visual Manipulation Relationship Dataset. Experimental results demonstrate that ROIGD performs much better in object overlapping scenes and at the meantime, remains comparable with stateoftheart grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset. Robotic experiments demonstrate that ROIGD can help robots grasp the target in singleobject and multiobject scenes with the overall success rates of 92.5% and 83.8% respectively.