Authors:
Rafal Staszak
and
Dominik Belter
Affiliation:
Institute of Control, Robotics and Information Engineering, Poznan University of Technology, Poznan and Poland
Keyword(s):
Object Pose Estimation, Convolutional Neural Networks, Deep Learning in Robotics.
Related
Ontology
Subjects/Areas/Topics:
Environmental Monitoring and Control
;
Hybrid Learning Systems
;
Image Processing
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Vision, Recognition and Reconstruction
Abstract:
In this research, we focus on the 6D pose estimation of known objects from the RGB image. In contrast to state of the art methods, which are based on the end-to-end neural network training, we proposed a hybrid approach. We use separate deep neural networks to: detect the object on the image, estimate the center of the object, and estimate the translation and ”in-place” rotation of the object. Then, we use geometrical relations on the image and the camera model to recover the full 6D object pose. As a result, we avoid the direct estimation of the object orientation defined in SO3 using a neural network. We propose the 4D-NET neural network to estimate translation and ”in-place” rotation of the object. Finally, we show results on the images generated from the Pascal VOC and ShapeNet datasets.