Keyframe-Based Visual-Inertial Online SLAM with Relocalization

Anton Kasyanov, Francis Engelmann, Jörg Stückler, Bastian Leibe
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'17), to appear

Complementing images with inertial measurements has become one of the most popular approaches to achieve highly accurate and robust real-time camera pose tracking. In this paper, we present a keyframe-based approach to visual-inertial simultaneous localization and mapping (SLAM) for monocular and stereo cameras. Our method is based on a real-time capable visual-inertial odometry method that provides locally consistent trajectory and map estimates. We achieve global consistency in the estimate through online loop-closing and non-linear optimization. Furthermore, our approach supports relocalization in a map that has been previously obtained and allows for continued SLAM operation. We evaluate our approach in terms of accuracy, relocalization capability and run-time efficiency on public benchmark datasets and on newly recorded sequences. We demonstrate state-of-the-art performance of our approach towards a visual-inertial odometry method in recovering the trajectory of the camera.

» Show BibTeX
@article{Kasyanov2017_VISLAM, title={{Keyframe-Based Visual-Inertial Online SLAM with Relocalization}}, author={Anton Kasyanov andFrancis Engelmann and J\"org St\"uckler and Bastian Leibe}, journal={ArXiv e-rpints:1702.02175}, year={2017} }



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