结合非精确建筑平面图的紧耦合SLAM
Tightly Coupled SLAM with Imprecise Architectural Plans
- Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg（卢森堡大学自动化与机器人研究组，安全、可靠与信任跨学科中心）
- Faculty of Science, Technology and Medicine, University of Luxembourg（卢森堡大学科学、技术与医学学院）
- I3A, Universidad de Zaragoza（阿拉维萨大学I3A）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对建筑平面图与真实室内环境存在偏差的问题，提出一种将基于激光雷达的SLAM与建筑平面图紧耦合的算法，通过多层语义表示实时估计全局对齐与结构偏差，在模拟和真实场景中均有效降低了定位与建图误差。
AI中文摘要：
在室内环境中导航的机器人通常可获取建筑平面图，这类平面图可作为先验知识提升机器人的定位与建图能力。尽管部分SLAM算法会利用这些平面图在真实环境中实现全局定位，但它们普遍忽略了一个关键挑战：“规划版”建筑设计往往与“建成版”真实环境存在偏差。为填补这一研究空白，我们提出了一种新型算法，该算法在存在偏差的场景下，将基于激光雷达的同步定位与建图（SLAM）与建筑平面图紧耦合。我们的方法采用多层语义表示，不仅能实现机器人定位，还能实时估计“规划版”与建成版环境之间的全局对齐情况与结构偏差。为验证我们的方法，我们在模拟数据集与真实数据集上开展了实验，结果表明该方法对最大35厘米、15度的结构偏差具备鲁棒性。在模拟环境中，我们的方法的定位误差平均比基线方法低43%；而在真实环境中，生成的建成版3D地图的平均对齐误差低7%。
英文摘要：
Robots navigating indoor environments often have access to architectural plans, which can serve as prior knowledge to enhance their localization and mapping capabilities. While some SLAM algorithms leverage these plans for global localization in real-world environments, they typically overlook a critical challenge: the "as-planned" architectural designs frequently deviate from the "as-built" real-world environments. To address this gap, we present a novel algorithm that tightly couples LIDAR-based simultaneous localization and mapping with architectural plans under the presence of deviations. Our method utilizes a multi-layered semantic representation to not only localize the robot, but also to estimate global alignment and structural deviations between "as-planned" and as-built environments in real-time. To validate our approach, we performed experiments in simulated and real datasets demonstrating robustness to structural deviations up to 35 cm and 15 degrees. On average, our method achieves 43% less localization error than baselines in simulated environments, while in real environments, the as-built 3D maps show 7% lower average alignment error
