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DNA-SLAM: Dense Noise Aware SLAM for ToF RGB-D Cameras

  • Oliver WasenmüllerEmail author
  • Mohammad Dawud Ansari
  • Didier Stricker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)

Abstract

SLAM with RGB-D cameras is a very active field 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 Projection 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.

Keywords

Motion Estimation Depth Image Geometric Error Camera Motion Pattern Projection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

426013_1_En_42_MOESM1_ESM.zip (16.9 mb)
Supplementary material 1 (zip 17268 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Oliver Wasenmüller
    • 1
    Email author
  • Mohammad Dawud Ansari
    • 2
  • Didier Stricker
    • 1
    • 2
  1. 1.German Research Center for Artificial Intelligence (DFKI)KaiserslauternGermany
  2. 2.University of KaiserslauternKaiserslauternGermany

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