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This library implements two different object tracking algorithms:
A Particle Filter based Tracker (https://am.is.tuebingen.mpg.de/publications/wuthrich_iicirs_2013)
This tracker can run on a pure CPU system or optionally run on GPU using CUDA 6.5 or later
inproceedings{wuthrich-iros-2013,
title = {Probabilistic Object Tracking Using a Range Camera},
author = {W{\"u}thrich, M. and Pastor, P. and Kalakrishnan, M. and Bohg, J. and Schaal, S.},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
pages = {3195-3202},
publisher = {IEEE},
month = nov,
year = {2013},
month_numeric = {11}
}
@inproceedings{jan_ICRA_2016,
title = {Depth-based Object Tracking Using a Robust Gaussian Filter},
author = {Issac, Jan and W{\"u}thrich, Manuel and Garcia Cifuentes, Cristina and Bohg, Jeannette and Trimpe, Sebastian and Schaal, Stefan},
booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016},
publisher = {IEEE},
month = may,
year = {2016},
url = {http://arxiv.org/abs/1602.06157},
month_numeric = {5}
}
All trackers require mesh models of the tracked objects in Wavefront (.obj) format. To get started, we recommend that you follow the instructions at https://github.com/bayesian-object-tracking/getting_started.
The cmake package uses Catkin. If you have installed ROS groovy or later, then you most likely have catkin installed already.
$ cd $HOME $ mkdir -p projects/tracking/src $ cd projects/tracking/src $ git clone git@github.com:filtering-library/fl.git $ git clone git@github.com:bayesian-object-tracking/dbot.git $ cd .. $ catkin_make -DCMAKE_BUILD_TYPE=Release -DDBOT_BUILD_GPU=On
If no CUDA enabled device is available, you can deactivate the GPU implementation via
$ catkin_make -DCMAKE_BUILD_TYPE=Release -DDBOT_BUILD_GPU=Off
Checkout the ros nodes of each tracker in dbot_ros package for exact usage of the filters.
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