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

  • Abstract
    • 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 is provided, for various scenes and motion profiles.
  • Code Binary
    • The binaries for the stereo VINS algorithm presented in this paper are available here (link).
  • Dataset
    • The datasets presented in the paper are available here (link).
  • Videos

  • Relevant Publications
  • C1. M. K. Paul, K. Wu, J. A. Hesch, E. D. Nerurkar, and S. I. Roumeliotis, "A Comparative Analysis of Tightly-coupled Monocular, Binocular, and Stereo VINS," International Conference on Robotics and Automation (ICRA), Singapore, May 29 - June 3, 2017. (link)