This paper presents a novel fusion technique for LiDAR Simultaneous Localization and Mapping (SLAM), aimed at improving localization and 3D mapping using LiDAR sensor. Our approach centers on the Inferred Attention Fusion (INAF) module, which integrates AI with geometric odometry. Utilizing the KITTI dataset's LiDAR data, INAF dynamically adjusts attention weights based on environmental feedback, enhancing the system's adaptability and measurement accuracy. This method advances the precision of both localization and 3D mapping, demonstrating the potential of our fusion technique to enhance autonomous navigation systems in complex scenarios.

翻译：本文提出了一种用于LiDAR即时定位与地图构建（SLAM）的新型融合技术，旨在利用LiDAR传感器提升定位与三维建图性能。我们的方法以推断注意力融合（INAF）模块为核心，将人工智能与几何里程计相结合。通过使用KITTI数据集的LiDAR数据，INAF能够根据环境反馈动态调整注意力权重，从而增强系统的适应性与测量精度。该方法显著提升了定位与三维建图的精确度，证明了我们的融合技术在复杂场景中增强自主导航系统性能的潜力。