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Prof. Dr. Didier Stricker

Dr. Alain Pagani

Dr. Gerd Reis

Eric Thil

Keonna Cunningham

Dr. Oliver Wasenmüller

Dr. Gabriele Bleser
Dr. Bruno Mirbach

Dr. Jason Raphael Rambach

Dr. Bertram Taetz
Dr. Muhammad Zeshan Afzal

Sk Aziz Ali

Mhd Rashed Al Koutayni
Murad Almadani
Alaa Alshubbak
Yuriy Anisimov

Jilliam Maria Diaz Barros

Ramy Battrawy
Hammad Butt

Mahdi Chamseddine
Steve Dias da Cruz

Fangwen Shu

Torben Fetzer

Ahmet Firintepe
Sophie Folawiyo

David Michael Fürst
Kamalveerkaur Garewal

Christiano Couto Gava
Leif Eric Goebel

Tewodros Amberbir Habtegebrial
Simon Häring
Khurram Hashmi

Jigyasa Singh Katrolia

Andreas Kölsch
Onorina Kovalenko

Stephan Krauß
Paul Lesur

Muhammad Jameel Nawaz Malik
Michael Lorenz
Markus Miezal

Mina Ameli

Nareg Minaskan Karabid
Mohammad Minouei

Pramod Murthy

Mathias Musahl

Peter Neigel

Manthan Pancholi
Qinzhuan Qian

Engr. Kumail Raza
Dr. Nadia Robertini
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar

Alexander Schäfer
Pascal Schneider

René Schuster

Mohamed Selim
Lukas Stefan Staecker

Dennis Stumpf

Yongzhi Su

Xiaoying Tan
Yaxu Xie

Dr. Vladislav Golyanik

Dr. Aditya Tewari

André Luiz Brandão
Accelerated DNA-SLAM for RGB-D images
Accelerated DNA-SLAM for RGB-D images
Mina Ameli, Oliver Wasenmüller, Mohammad Reza Soheili, Jamshid Shanbehzadeh, Didier Stricker
ACM International Conference on Image and Graphics Processing (ICIGP-2018), February 24-26, Hong Kong, China
- Abstract:
- In the highly active research field of Simultaneous Localization And Mapping (SLAM), RGB-D images have been a major interest to use. Real-time SLAM for RGB-D images is of great importance since dense methods using all the depth and intensity values showed superior performance in the past. Due to development of GPU and CPU technologies, the real-time implementation of the mentioned algorithms is no longer an impassable problem. In this paper, we present an acceleration approach for the DNA-SLAM algorithm. We argue some possible challenges while converting the CPU implemented algorithm to the GPU. Finally, runtime evaluation and improvements are shown on the public CoRBS dataset.