Panoramic annular SLAM with loop closure and global optimization
Hao Chen, Weijian Hu, Kailun Yang, Jian Bai, Kaiwei Wang
Abstract
In this paper, we propose panoramic annular simultaneous localization and mapping (PA-SLAM), a visual SLAM system based on a panoramic annular lens. A hybrid point selection strategy is put forward in the tracking front end, which ensures repeatability of key points and enables loop closure detection based on the bag-of-words approach. Every detected loop candidate is verified geometrically, and the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">S</mml:mi> <mml:mi mathvariant="normal">i</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mn>3</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> 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 data sets 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.