PAPL-SLAM: Principal Axis-Anchored Monocular Point-Line SLAM

In point-line Simultaneous Localization and Mapping (SLAM) systems, the utilization of line structural information and the optimization of lines are two significant problems. The former is usually addressed through structural regularities, while the latter typically involves using minimal parameter representations of lines in optimization. However, separating these two steps leads to the loss of constraint information to each other. To solve both problems, we anchor lines with similar directions to one principal axis. Precisely, our method models the line-axis probabilistic data association using the Expectation Maximization (EM) algorithm and provides the pipelines for axis creation, updating, and optimization, enhancing the system's robustness and avoiding mismatch. Our system can optimize $n$ co-directional lines with only $n+2$ parameters, significantly reducing the number of line parameters to be optimized and enabling rapid mapping and tracking. Additionally, considering that most real-world scenes conform to the Atlanta World (AW) hypothesis, we provide an AW constraint by detecting structural lines based on vertical priors and vanishing points. Experimental results and ablation studies on various indoor and outdoor datasets demonstrate the effectiveness of our system.