Deep-Learning based Robust Edge Detection for Point Pair Feature-based Pose Estimation with Multiple Edge Appearance Models

Abstract

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 Feature-based Pose Estimation with Multiple Edge Appearance Models (PPF-MEAM) 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 Deep-Learning based Robust Edge Detection (DLED) for PPF-MEAM 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 PPF-MEAM under different light conditions.