Monocular visual odometry: Sparse joint optimisation or dense alternation?

Lukas Platinsky, Andrew J. Davison, Stefan Leutenegger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Real-time monocular SLAM is increasingly mature and entering commercial products. However, there is a divide between two techniques providing similar performance. Despite the rise of 'dense' and 'semi-dense' methods which use large proportions of the pixels in a video stream to estimate motion and structure via alternating estimation, they have not eradicated feature-based methods which use a significantly smaller amount of image information from keypoints and retain a more rigorous joint estimation framework. Dense methods provide more complete scene information, but in this paper we focus on how the amount of information and different optimisation methods affect the accuracy of local motion estimation (monocular visual odometry). This topic becomes particularly relevant after the recent results from a direct sparse system. We propose a new method for fairly comparing the accuracy of SLAM frontends in a common setting. We suggest computational cost models for an overall comparison which indicates that there is relative parity between the approaches at the settings allowed by current serial processors when evaluated under equal conditions.

Original languageEnglish
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5126-5133
Number of pages8
ISBN (Electronic)9781509046331
DOIs
StatePublished - 21 Jul 2017
Externally publishedYes
Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
Duration: 29 May 20173 Jun 2017

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Country/TerritorySingapore
CitySingapore
Period29/05/173/06/17

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