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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
DNA-SLAM: Dense Noise Aware SLAM for ToF RGB-D Cameras
DNA-SLAM: Dense Noise Aware SLAM for ToF RGB-D Cameras
Oliver Wasenmüller, Mohammad Dawud Ansari, Didier Stricker
Asian Conference on Computer Vision Workshop Asian Conference on Computer Vision Workshop (ACCV workshop-16), Taipeh, Taiwan, Province of China
- Abstract:
- SLAM with RGB-D cameras is a very active eld in Computer Vision as well as Robotics. Dense methods using all depth and intensity information showed best results in the past. However, usually they were developed and evaluated with RGB-D cameras using Pattern Pro- jection like the Kinect v1 or Xtion Pro. Recently, Time-of-Flight (ToF) cameras like the Kinect v2 or Google Tango were released promising higher quality. While the overall accuracy increases for these ToF cameras, noisy pixels are introduced close to discontinuities, in the image corners and on dark/glossy surfaces. These inaccuracies need to be specially addressed for dense SLAM. Thus, we present a new Dense Noise Aware SLAM (DNA-SLAM), which considers explicitly the noise characteristics of ToF RGB-D cameras with a sophisticated weighting scheme. In a rigorous evaluation on public benchmarks we show the superior accuracy of our algorithm compared to the state-of-the-art.