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Research Article | Open Access

Benchmarking visual SLAM methods in mirror environments

School of Computer Science and Informatics, Cardiff University, Abacws Building, Senghennydd Rd, Cardiff CF24 4AG, UK
School of Engineering, Cardiff University, Queen’s Buildings, The Parade, Cardiff CF24 3AA, UK
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Graphical Abstract

Abstract

Visual simultaneous localisation and mapping (vSLAM) finds applications for indoor and outdoor navigation that routinely subjects it to visual complexities, particularly mirror reflections. The effect of mirror presence (time visible and its average size in the frame) was hypothesised to impact localisation and mapping performance, with systems using direct techniques expected to perform worse. Thus, a dataset, MirrEnv, of image sequences recorded in mirror environments, was collected, and used to evaluate the performance of existing representative methods. RGBD ORB-SLAM3 and BundleFusion appear to show moderate degradation of absolute trajectory error with increasing mirror duration, whilst the remaining results did not show significantly degraded localisation performance. The mesh maps generated proved to be very inaccurate, with real and virtual reflections colliding in the reconstructions. A discussion is given of the likely sources of error and robustness in mirror environments, outlining future directions for validating and improving vSLAM performance in the presence of planar mirrors. The MirrEnv dataset is available at https://doi.org/10.17035/d.2023.0292477898.

Keywords

visual simultaneous localisation and mapping (vSLAM)mirrorlocalisationmappingreflectiondataset

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Computational Visual Media
Volume 10 Issue 2,
April 2024
Pages 215-241
DOI: 10.1007/s41095-022-0329-x
Cite this article:
Herbert P, Wu J, Ji Z, et al. Benchmarking visual SLAM methods in mirror environments. Computational Visual Media, 2024, 10(2): 215-241. https://doi.org/10.1007/s41095-022-0329-x

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Received: 27 July 2022
Accepted: 18 December 2022
Published: 03 January 2024
© The Author(s) 2023.

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