Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual 
odometry approach for endoscopic capsule robots

Turan, Mehmet; Almalioglu, Yasin; Araujo, Helder; Konukoglu, Ender; Sitti, Metin

doi:10.1016/j.neucom.2017.10.014

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Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots

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Turan, Mehmet
Dept. Physical Intelligence, Max Planck Institute for Intelligent Systems, Max Planck Society;
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Sitti, Metin
Dept. Physical Intelligence, Max Planck Institute for Intelligent Systems, Max Planck Society;
Dept. of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA;

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Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., & Sitti, M. (2018). Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots. Neurocomputing, 275, 1861-1870. doi:10.1016/j.neucom.2017.10.014.

Cite as: https://hdl.handle.net/21.11116/0000-0003-80CC-6

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