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Linear RGB-D SLAM for Planar Environments

  • Pyojin Kim
  • Brian Coltin
  • H. Jin KimEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11208)

Abstract

We propose a new formulation for including orthogonal planar features as a global model into a linear SLAM approach based on sequential Bayesian filtering. Previous planar SLAM algorithms estimate the camera poses and multiple landmark planes in a pose graph optimization. However, since it is formulated as a high dimensional nonlinear optimization problem, there is no guarantee the algorithm will converge to the global optimum. To overcome these limitations, we present a new SLAM method that jointly estimates camera position and planar landmarks in the map within a linear Kalman filter framework. It is rotations that make the SLAM problem highly nonlinear. Therefore, we solve for the rotational motion of the camera using structural regularities in the Manhattan world (MW), resulting in a linear SLAM formulation. We test our algorithm on standard RGB-D benchmarks as well as additional large indoors environments, demonstrating comparable performance to other state-of-the-art SLAM methods without the use of expensive nonlinear optimization.

Keywords

Linear SLAM Manhattan world Bayesian filtering 

Notes

Acknowledgements

This work was supported by the Samsung Smart Campus Research Center (0115-20170013) and Samsung Research, Samsung Electronics Co.,Ltd. Special thanks to Phi-Hung Le for his assistance with the DPP-SLAM code.

Supplementary material

Supplementary material 1 (mp4 50888 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.ASRISeoul National UniversitySeoulSouth Korea
  2. 2.SGT, Inc., NASA Ames Research CenterMountain ViewUSA

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