For grasping and manipulation with robot arms, knowing the current pose of the arm is crucial for successful controlling its motion. Often, pose estimations can be acquired from encoders inside the arm, but they can have significant inaccuracy which makes the use of additional techniques necessary. In this master thesis, a novel approach of robot arm pose estimation is presented, that works on single depth images without the need of prior foreground segmentation or other preprocessing steps. A random regression forest is used, which is trained only on synthetically generated data. The approach improves former work by Bohg et al. by considerably reducing the computational effort both at training and test time. The forest in the new method directly estimates the desired joint angles while in the former approach, the forest casts 3D position votes for the joints, which then have to be clustered and fed into an iterative inverse kinematic process to finally get the joint angles. To improve the estimation accuracy, the standard training objective of the forest training is replaced by a specialized function that makes use of a model-dependent distance metric, called DISP. Experimental results show that the specialized objective indeed improves pose estimation and it is shown that the method, despite of being trained on synthetic data only, is able to provide reasonable estimations for real data at test time.
| Author(s): | Felix Widmaier |
| Year: | 2015 |
| Month: | May |
| Project(s): |
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| BibTeX Type: | Master Thesis (mastersthesis) |
| Electronic Archiving: | grant_archive |
| Language: | English |
| School: | Eberhard-Karls-Universität Tübingen |
| Attachments: | |
BibTeX
@mastersthesis{felixw_masterthesis,
title = {Robot Arm Tracking with Random Decision Forests},
abstract = {For grasping and manipulation with robot arms, knowing the current pose of the arm is crucial
for successful controlling its motion. Often, pose estimations can be acquired from encoders
inside the arm, but they can have significant inaccuracy which makes the use of additional
techniques necessary.
In this master thesis, a novel approach of robot arm pose estimation is presented, that works on
single depth images without the need of prior foreground segmentation or other preprocessing
steps.
A random regression forest is used, which is trained only on synthetically generated data.
The approach improves former work by Bohg et al. by considerably reducing the computational
effort both at training and test time. The forest in the new method directly estimates the
desired joint angles while in the former approach, the forest casts 3D position votes for the
joints, which then have to be clustered and fed into an iterative inverse kinematic process to
finally get the joint angles.
To improve the estimation accuracy, the standard training objective of the forest training is
replaced by a specialized function that makes use of a model-dependent distance metric, called
DISP.
Experimental results show that the specialized objective indeed improves pose estimation and
it is shown that the method, despite of being trained on synthetic data only, is able to
provide reasonable estimations for real data at test time.
},
school = {Eberhard-Karls-Universität Tübingen},
month = may,
year = {2015},
author = {Widmaier, Felix},
month_numeric = {5}
}