A New Edge Patch with Rotation Invariance for Object Detection and Pose Estimation Local patchbased methods of object detection and pose estimation are promising. However, to the best of the authorsxe2x80x99 knowledge, traditional redgreenblue and depth (RGBD) patches contain scene interference (foreground occlusion and background clutter) and have little rotation invariance. To solve these problems, a new edge patch is proposed and experimented with in this study. The edge patch is a local sampling RGBD patch centered at the edge pixel of the depth image. According to the normal direction of the depth edge, the edge patch is sampled along a canonical orientation, making it rotation invariant. Through a process of depth detection, scene interference is eliminated from the edge patch, which improves the robustness. The framework of the edge patchbased method is described, and the method was evaluated on three public datasets. Compared with existing methods, the proposed method achieved a higher average F1score (0.956) on the Tejani dataset and a better average detection rate (62%) on the Occlusion dataset, even in situations of serious scene interference. These results showed that the proposed method has higher detection accuracy and stronger robustness.

DeepLearning based Robust Edge Detection for Point Pair Featurebased Pose Estimation with Multiple Edge Appearance Models To realize a robotic bin picking system, pose estimation for the objects randomly piled up in a bin is necessary. For various types of objects, many pose estimation algorithms have been proposed so far. Point Pair Featurebased Pose Estimation with Multiple Edge Appearance Models (PPFMEAM) has been proposed for estimating the pose of industrial parts including some parts whose point clouds are defective in our previous work. Although this method shows high performance in pose estimation under a constant environment, its performance drops under the changing light conditions without tuning parameters. To overcome this problem, we propose DeepLearning based Robust Edge Detection (DLED) for PPFMEAM to make it robust to changes of the light. The effectiveness of DLED is proved by the edge detection experiment under different light conditions. Moreover, the pose estimation experiment proves that DLED could improve the pose estimation performance of PPFMEAM under different light conditions.