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.
CITATION STYLE
Paul, M. K., Wu, K., Hesch, J. A., Nerurkar, E. D., & Roumeliotis, S. I. (2017). A comparative analysis of tightly-coupled monocular, binocular, and stereo VINS. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 165–172). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICRA.2017.7989022
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