用主轴锚定法统一优化点线特征，提升单目定位精度与速度。
PAPL-SLAM: Principal Axis-Anchored Monocular Point-Line SLAM
- 将相似方向的直线锚定到主轴，统一优化$ n+2 $个参数
- 在多个数据集上实现更高精度与更快收敛速度
- 适合室内室外复杂场景下的实时三维重建
在点线SLAM系统中，线结构信息的利用与线优化是两大关键问题。传统方法分别处理，导致相互约束信息丢失。本文将方向相近的线锚定至主轴，采用$ n+2 $个参数联合优化$ n $条线，同时解决两者。该方法融合场景结构先验，可灵活适配不同世界假设，大幅减少需优化的线参数量，实现快速精准的建图与跟踪。为增强鲁棒性，提出线-轴概率数据关联模型，并给出轴的创建、更新与优化算法。考虑到多数真实场景符合亚特兰大世界假设，设计基于垂直先验与消失点的结构化线检测策略。在多个室内外数据集上的实验与消融研究验证了系统有效性。
原文摘要 · Abstract (English)
In point-line 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. We anchor lines with similar directions to a principal axis and optimize them with $n+2$ parameters for $n$ lines, solving both problems together. Our method considers scene structural information, which can be easily extended to different world hypotheses while significantly reducing the number of line parameters to be optimized, enabling rapid and accurate mapping and tracking. To further enhance the system's robustness and avoid mismatch, we have modeled the line-axis probabilistic data association and provided the algorithm for axis creation, updating, and optimization. Additionally, considering that most real-world scenes conform to the Atlanta World hypothesis, we provide a structural line detection strategy based on vertical priors and vanishing points. Experimental results and ablation studies on various indoor and outdoor datasets demonstrate the effectiveness of our system.
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