Object detection and Autoencoder-based 6D pose estimation for highly cluttered Bin Picking

Abstract

Bin picking is a core problem in industrial environments and robotics, with its main module as 6D pose estimation. However, industrial depth sensors have a lack of accuracy when it comes to small objects. Therefore, we propose a framework for pose estimation in highly cluttered scenes with small objects, which mainly relies on RGB data and makes use of depth information only for pose refinement. In this work, we compare synthetic data generation approaches for object detection and pose estimation and introduce a pose filtering algorithm that determines the most accurate estimated poses. We will make our real dataset for object detection available with the paper.

Deep learning-based multiple animal pose estimation

“…Pose estimation is a active field of research in computer vision and has potential applications in human behavior analysis [1][2][3][4][5], virtual reality [6,7], action recognition [8][9][10][11][12][13][14][15], segmentation [16][17][18][19][20], object detection [21][22][23][24][25][26][27][28], autonomous driving [29][30][31], tracking [32][33][34][35][36][37][38][39][39][40][41][42][43][44][45], medical imaging [46][47][48][49] and facial emotion recognition…”