The recent success of hybrid methods in monocular odometry has led to many attempts to generalize the performance gains to hybrid monocular SLAM. However, most attempts fall short in several respects, with the most prominent issue being the need for two different map representations (local and global maps), with each requiring different, computationally expensive, and often redundant processes to maintain. Moreover, these maps tend to drift with respect to each other, resulting in contradicting pose and scene estimates, and leading to catastrophic failure. In this paper, we propose a novel approach that makes use of descriptor sharing to generate a single inverse depth scene representation. This representation can be used locally, queried globally to perform loop closure, and has the ability to re-activate previously observed map points after redundant points are marginalized from the local map, eliminating the need for separate and redundant map maintenance processes. The maps generated by our method exhibit no drift between each other, and can be computed at a fraction of the computational cost and memory footprint required by other monocular SLAM systems. Despite the reduced resource requirements, the proposed approach maintains its robustness and accuracy, delivering performance comparable to state-of-the-art SLAM methods (e.g., LDSO, ORB-SLAM3) on the majority of sequences from well-known datasets like EuRoC, KITTI, and TUM VI. The source code is available at: https://github.com/AUBVRL/fslam_ros_docker.

翻译：近期混合方法在单目里程计中的成功，促使许多研究者尝试将性能提升推广至混合单目SLAM。然而，大多数尝试在多个方面存在不足，最显著的问题是需要两种不同的地图表示（局部地图和全局地图），每种地图都需要独立且计算密集的维护流程，且流程之间常存在冗余。此外，这两种地图之间容易产生漂移，导致位姿与场景估计相互矛盾，最终引发系统灾难性失效。本文提出一种新颖方法，通过描述子共享生成单一的逆深度场景表示。该表示既可用于局部定位，也可作为全局查询实现闭环检测，同时能够在冗余地图点被边缘化后重新激活先前观测到的地图点，从而消除独立且冗余的地图维护流程。本方法生成的地图之间不存在相互漂移，且计算开销与内存占用仅为其他单目SLAM系统的极小部分。尽管资源需求降低，本方法仍保持鲁棒性与准确性，在EuRoC、KITTI和TUM VI等知名数据集的大部分序列上，其性能可与当前最先进的SLAM方法（如LDSO、ORB-SLAM3）相媲美。源代码获取地址：https://github.com/AUBVRL/fslam_ros_docker。