PAPL-SLAM：主轴锚定的单目点线SLAM
PAPL-SLAM: Principal Axis-Anchored Monocular Point-Line SLAM
AI总结：
本文提出PAPL-SLAM，通过将方向相似的线锚定到主轴并用n+2个参数联合优化n条线，同时解决线结构信息利用与线参数优化问题，并引入线-轴概率数据关联和基于Atlanta World假设的结构线检测策略，在室内外数据集上验证了系统的有效性。
摘要：
在点线SLAM系统中，线结构信息的利用和线的优化是两个重要问题。前者通常通过结构规律性来处理，而后者通常涉及在优化中使用线的最小参数表示。然而，将这两个步骤分开会导致彼此之间约束信息的丢失。我们将方向相似的线锚定到一个主轴上，并用$n+2$个参数优化$n$条线，同时解决这两个问题。我们的方法考虑了场景结构信息，可以轻松扩展到不同的世界假设，同时显著减少了需要优化的线参数数量，从而实现快速准确的地图构建和跟踪。为了进一步增强系统的鲁棒性并避免误匹配，我们对线-轴概率数据关联进行了建模，并提供了轴的创建、更新和优化算法。此外，考虑到大多数真实世界场景符合Atlanta World假设，我们提供了一种基于垂直先验和消失点的结构线检测策略。在各种室内和室外数据集上的实验结果和消融研究证明了我们系统的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
