HDRFusion: HDR SLAM using a low-cost auto-exposure RGB-D sensor

Shuda Li, Ankur Handa, Yang Zhang, Andrew Calway

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

9 Citations (Scopus)
376 Downloads (Pure)

Abstract

Most dense RGB/RGB-D SLAM systems require the brightness of 3-D points observed from different viewpoints to be constant. However, in reality, this assumption is dif- ficult to meet even when the surface is Lambertian and il- lumination is static. One cause is that most cameras auto- matically tune exposure to adapt to the wide dynamic range of scene radiance, violating the brightness assumption. We describe a novel system - HDRFusion - which turns this ap- parent drawback into an advantage by fusing LDR frames into an HDR textured volume using a standard RGB-D sen- sor with auto-exposure (AE) enabled. The key contribution is the use of a normalised metric for frame alignment which is invariant to changes in exposure time. This enables robust tracking in frame-to-model mode and also compensates the exposure accurately so that HDR texture, free of artefacts, can be generated online. We demonstrate that the track- ing robustness and accuracy is greatly improved by the ap- proach and that radiance maps can be generated with far greater dynamic range of scene radiance.
Original languageEnglish
Title of host publication2016 Fourth International Conference on 3D Vision (3DV 2016)
Subtitle of host publicationProceedings of a meeting held 25-28 October 2016, Stanford, CA, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages314-322
Number of pages9
ISBN (Electronic)9781509054077
ISBN (Print)9781509054084
DOIs
Publication statusPublished - 19 Dec 2016
Event2016 International Conference on 3D Vision - University of Stanford, California, United States
Duration: 25 Oct 201628 Oct 2016
http://3dv.stanford.edu/index.html

Conference

Conference2016 International Conference on 3D Vision
Abbreviated title3D Vision 2016
CountryUnited States
CityCalifornia
Period25/10/1628/10/16
Internet address

Keywords

  • high dynamic range
  • 3-D mapping and tracking
  • auto exposure
  • RGB-D cameras

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    Li, S., Handa, A., Zhang, Y., & Calway, A. (2016). HDRFusion: HDR SLAM using a low-cost auto-exposure RGB-D sensor. In 2016 Fourth International Conference on 3D Vision (3DV 2016): Proceedings of a meeting held 25-28 October 2016, Stanford, CA, USA (pp. 314-322). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/3DV.2016.40