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ORB-SLAM-CNN: Lessons in Adding Semantic Map Construction to Feature-Based SLAM

  • Andrew M. WebbEmail author
  • Gavin Brown
  • Mikel Luján
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11649)

Abstract

Recent work has integrated semantics into the 3D scene models produced by visual SLAM systems. Though these systems operate close to real time, there is lacking a study of the ways to achieve real-time performance by trading off between semantic model accuracy and computational requirements. ORB-SLAM2 provides good scene accuracy and real-time processing while not requiring GPUs [1]. Following a ‘single view’ approach of overlaying a dense semantic map over the sparse SLAM scene model, we explore a method for automatically tuning the parameters of the system such that it operates in real time while maximizing prediction accuracy and map density.

Keywords

Online parameter tuning SLAM Semantic segmentation 

Notes

Acknowledgements

The authors gratefully acknowledge the support of the EPSRC grants LAMBDA (EP/N035127/1), PAMELA (EP/K008730/1), and RAIN (EP/R026084/1).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK

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