A Comparative Analysis of Tightly-Coupled Monocular, Binocular, and Stereo VINS

Mrinal Kanti Paul1, Kejian Wu2, Joel A. Hesch3, Esha Nerurkar2, Stergios Roumeliotis2

  • 1University of Minnesota - Twin Cities
  • 2University of Minnesota
  • 3Google Inc.



Regular Papers


09:55 - 11:10 | Tue 30 May | Room 4311/4312 | TUA3

Computer Vision 1

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In this paper, a sliding-window two-camera vision-aided inertial navigation system (VINS) is presented in the square-root inverse domain. The performance of the system is assessed for the cases where feature matches across the two-camera images are processed with or without any stereo constraints (i.e., stereo vs. binocular). To support the comparison results, a theoretical analysis on the information gain when transitioning from binocular to stereo is also presented. Additionally, the advantage of using a two-camera (both stereo and binocular) system over a monocular VINS is assessed. Furthermore, the impact on the achieved accuracy of different image-processing frontends and estimator design choices is quantified. Finally, a thorough evaluation of the algorithm's processing requirements, which runs in real-time on a mobile processor, as well as its achieved accuracy as compared to alternative approaches is provided, for various scenes and motion profiles.

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