  In this paper, we propose panoramic annular simultaneous localization and
mapping (PA-SLAM), a visual SLAM system based on panoramic annular lens. A
hybrid point selection strategy is put forward in the tracking front-end, which
ensures repeatability of keypoints and enables loop closure detection based on
the bag-of-words approach. Every detected loop candidate is verified
geometrically and the $Sim(3)$ relative pose constraint is estimated to perform
pose graph optimization and global bundle adjustment in the back-end. A
comprehensive set of experiments on real-world datasets demonstrates that the
hybrid point selection strategy allows reliable loop closure detection, and the
accumulated error and scale drift have been significantly reduced via global
optimization, enabling PA-SLAM to reach state-of-the-art accuracy while
maintaining high robustness and efficiency.
