Prof. Dr. Didier Stricker
Dr. Alain Pagani
Dr. Gerd Reis
M.A. Eric Thil
Nicole Stephanie Buhlinger
Keonna Cunningham
Dr. Oliver Wasenmüller
Dr. Gabriele Bleser
Dr. Bertram Taetz
M.Sc Rashed Al Koutayni
Yuriy Anisimov
Ramy Battrawy
Hammad Butt
Mahdi Chamseddine
Steve Dias da Cruz
M.Sc. Fangwen Shu
Torben Fetzer
Michael Fürst
Christiano Couto Gava
Vladislav Golyanik
Hartmut Feld
Andreas Kölsch
Onorina Kovalenko
Stephan Krauß
Paul Lesur
Muhammad Jameel Nawaz Malik
Nareg Minaskan Karabid
Mathias Musahl
Peter Neigel
Manthan Pancholi
Jason Raphael Rambach
María Alejandra Sánchez Marín
Kripasindhu Sarkar
Alexander Schäfer
René Schuster
Mohamed Selim
M.Sc. Dennis Stumpf
Yongzhi Su
Xiaoying Tan
Aditya Tewari
André Luiz Brandão
Ahmet Firintepe
Sk Aziz Ali
Jilliam Maria Diaz Barros
Tewodros Amberbir Habtegebrial
Mina Ameli
Pramod Murthy
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.