Visual Simultaneous Localization and Mapping (V-SLAM) is pivotal for precise positioning and mapping. However, visual data from crowd-sourced datasets often contains deficiencies that may lead to positioning errors. Despite existing optimization techniques, current algorithms do not adequately adapt to varied data in vehicle driving scenarios. To address this gap, this study introduces a novel SLAM framework (SLG-SLAM). This framework refines trajectories by integrating semantic information, laser point cloud, and global navigation satellite system (GNSS) data into V-SLAM. Initial trajectory estimates are made after filtering out dynamic targets and are subsequently refined with matched laser point clouds, then corrected for scale and direction using GNSS. The efficacy of this approach is assessed using four public datasets and one self-collected dataset, showing significant enhancements across all datasets. The proposed method reduces the mean absolute trajectory error by 43.50% on the KITTI dataset and 14.91% on the MVE dataset compared to the baseline. Unlike the baseline, which fails on three other datasets, the proposed method successfully performs localization and mapping. Additionally, compared to three other single-source methods (DynaSLAM, MCL, MVSLAM), the proposed method consistently outperforms, demonstrating its superior adaptability and effectiveness.

中文翻译：

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SLG-SLAM：一个集成的 SLAM 框架，使用语义信息、激光和 GNSS 数据提高准确性


视觉同步定位和地图构建 （V-SLAM） 对于精确定位和地图构建至关重要。然而，来自众包数据集的视觉数据通常包含可能导致定位误差的缺陷。尽管存在优化技术，但当前的算法无法充分适应车辆驾驶场景中的各种数据。为了解决这一差距，本研究引入了一种新的 SLAM 框架 （SLG-SLAM）。该框架通过将语义信息、激光点云和全球导航卫星系统 （GNSS） 数据集成到 V-SLAM 中来优化轨迹。在过滤掉动态目标后进行初始轨迹估计，然后使用匹配的激光点云进行细化，然后使用 GNSS 校正比例和方向。使用四个公共数据集和一个自行收集的数据集评估了这种方法的有效性，显示了所有数据集的显著增强。与基线相比，所提出的方法在 KITTI 数据集上将平均绝对轨迹误差降低了 43.50%，在 MVE 数据集上降低了 14.91%。与在其他三个数据集上失败的基线不同，所提出的方法成功地执行了定位和映射。此外，与其他三种单一来源方法 （DynaSLAM 、 MCL 、 MVSLAM ）相比，所提出的方法始终表现出色，展示了其卓越的适应性和有效性。