6D Pose Estimation for Flexible Production with Small Lot Sizes based on CAD Models using Gaussian Process Implicit Surfaces

2020 ◽  
Author(s):  
Jianjie Lin ◽  
Markus Rickert ◽  
Alois Knoll
Keyword(s):  
Gaussian Process ◽  
Pose Estimation ◽  
Implicit Surfaces ◽  
Flexible Production ◽  
Cad Models ◽  
Lot Sizes

scholarly journals Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Haopeng Zhang ◽  
Cong Zhang ◽  
Zhiguo Jiang ◽  
Yuan Yao ◽  
Gang Meng
Keyword(s):  
Gaussian Process ◽  
Pose Estimation ◽  
Gaussian Process Regression ◽  
Regression Function ◽  
Mean Value ◽  
Training Data ◽  
Simulated Image ◽  
Lighting Conditions ◽  
Mean Function ◽  
Imaging Sensors

In this paper, we address the problem of vision-based satellite recognition and pose estimation, which is to recognize the satellite from multiviews and estimate the relative poses using imaging sensors. We propose a vision-based method to solve these two problems using Gaussian process regression (GPR). Assuming that the regression function mapping from the image (or feature) of the target satellite to its category or pose follows a Gaussian process (GP) properly parameterized by a mean function and a covariance function, the predictive equations can be easily obtained by a maximum-likelihood approach when training data are given. These explicit formulations can not only offer the category or estimated pose by the mean value of the predicted output but also give its uncertainty by the variance which makes the predicted result convincing and applicable in practice. Besides, we also introduce a manifold constraint to the output of the GPR model to improve its performance for satellite pose estimation. Extensive experiments are performed on two simulated image datasets containing satellite images of 1D and 2D pose variations, as well as different noises and lighting conditions. Experimental results validate the effectiveness and robustness of our approach.

scholarly journals Object shape estimation and modeling, based on sparse Gaussian process implicit surfaces, combining visual data and tactile exploration

2020 ◽  
Vol 126 ◽  
pp. 103433
Author(s):  
Gabriela Zarzar Gandler ◽  
Carl Henrik Ek ◽  
Mårten Björkman ◽  
Rustam Stolkin ◽  
Yasemin Bekiroglu
Keyword(s):  
Gaussian Process ◽  
Implicit Surfaces ◽  
Visual Data ◽  
Shape Estimation ◽  
Object Shape
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