  In this paper, we propose PA-SLAM, a monocular panoramic annular visual SLAM
system with loop closure and global optimization. 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.
