FR-SLAM：一种基于楼层平面图配准的SLAM改进方法
FR-SLAM: A SLAM Improvement Method Based on Floor Plan Registration
AI总结：
FR-SLAM通过基于形态学的楼层平面图配准算法，实现快速地图获取与高效路径规划，提高配准精度并缩短到达目标时间。
摘要：
同步定位与建图（SLAM）技术能够实现环境地图构建与定位，是移动机器人室内自主导航的关键技术。传统SLAM方法在室内导航过程中通常需要遍历所有房间以获取完整地图，导致路径规划时间较长，到达目标点耗时增加。此外，运动过程中的累积误差会导致机器人定位不准确，影响导航效率。本文提出了一种基于楼层平面图配准的改进SLAM方法FR-SLAM，利用基于形态学的楼层平面图配准算法对原始楼层平面图进行对齐与变换。该方法有助于快速获取全面的运动地图并进行高效路径规划，使机器人在更短时间内迅速导航至目标位置。为提高配准与机器人运动定位精度，采用实时更新策略，将当前位置的建筑结构与地图进行比较，动态更新楼层平面图配准结果以实现精确定位。在真实与模拟数据集上的对比测试表明，与其他基准算法相比，该方法在楼层平面图配准精度上更高，到达目标位置的时间消耗更短。
英文摘要：
Simultaneous Localization and Mapping (SLAM) technology enables the construction of environmental maps and localization, serving as a key technique for indoor autonomous navigation of mobile robots. Traditional SLAM methods typically require exhaustive traversal of all rooms during indoor navigation to obtain a complete map, resulting in lengthy path planning times and prolonged time to reach target points. Moreover, cumulative errors during motion lead to inaccurate robot localization, impacting navigation efficiency.This paper proposes an improved SLAM method, FR-SLAM, based on floor plan registration, utilizing a morphology-based floor plan registration algorithm to align and transform original floor plans. This approach facilitates the rapid acquisition of comprehensive motion maps and efficient path planning, enabling swift navigation to target positions within a shorter timeframe. To enhance registration and robot motion localization accuracy, a real-time update strategy is employed, comparing the current position's building structure with the map and dynamically updating floor plan registration results for precise localization. Comparative tests conducted on real and simulated datasets demonstrate that, compared to other benchmark algorithms, this method achieves higher floor plan registration accuracy and shorter time consumption to reach target positions.
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。
