Prof. Dr. Didier Stricker
Dr. Alain Pagani
Dr. Gerd Reis
Eric Thil
Nicole Stephanie Buhlinger
Keonna Cunningham
Jason Raphael Rambach
Dr. Oliver Wasenmüller
Dr. Gabriele Bleser
Dr. Bertram Taetz
Sk Aziz Ali
Rashed Al Koutayni
Yuriy Anisimov
Jilliam Maria Diaz Barros
Ramy Battrawy
Hammad Butt
Mahdi Chamseddine
Steve Dias da Cruz
Fangwen Shu
Torben Fetzer
Michael Fürst
Christiano Couto Gava
Tewodros Amberbir Habtegebrial
Hartmut Feld
Jigyasa Katrolia
Andreas Kölsch
Onorina Kovalenko
Stephan Krauß
Paul Lesur
Muhammad Jameel Nawaz Malik
Mina Ameli
Nareg Minaskan Karabid
Pramod Murthy
Mathias Musahl
Peter Neigel
Manthan Pancholi
María Alejandra Sánchez Marín
Kripasindhu Sarkar
Alexander Schäfer
René Schuster
Mohamed Selim
Dennis Stumpf
Yongzhi Su
Xiaoying Tan
André Luiz Brandão
Ahmet Firintepe
Vladislav Golyanik
Aditya Tewari
SlamCraft: Dense Planar RGB Monocular SLAM
SlamCraft: Dense Planar RGB Monocular SLAM
Proceedings of. IAPR Conference on Machine Vision Applications (MVA-2019) May 27-31 Tokyo Japan IAPR 2019 .
- Abstract:
- Monocular Simultaneous Localization and Mapping (SLAM) approaches have progressed signifcantly over the last two decades. However, keypoint-based approaches only provide limited structural information in a 3D point cloud which does not fulfil the requirements of applications such as Augmented Reality (AR). SLAM systems that provide dense environment maps are either computationally intensive or require depth information from additional sensors. In this paper, we use a deep neural network that estimates planar regions from RGB input images and fuses its output iteratively with the point cloud map of a SLAM system to cre- ate an efficient monocular planar SLAM system. We present qualitative results of the created maps, as well as an evaluation of the tracking accuracy and runtime of our approach.