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.

LSD Based Vision Detection System for Industrial Robot under Complex Illumination Conditions In this paper, we present an object vision detection and localization method approach to the industrial robotic system under complex illumination conditions. In industrial applications of machine vision, many of feature extraction algorithms depend on edge detection, which can be affected by ambient illumination variation and loses efficacy. We propose a robust method that combines image enhancement, edge detection and feature information extraction to detect and locate target components. The key idea is to use a histogram key point constrained homomorphic filtering to enhance the image followed by line and arc segments feature detection. Finally, the experimental results on probe endface image dataset and steelmaking automation manufacture demonstrate the ability of the proposed method to perform more robust and accurate effectiveness under illumination variant conditions.