  Existing Simultaneous Localization and Mapping (SLAM) approaches are limited
in their scalability due to growing map size in long-term robot operation.
Moreover, processing such maps for localization and planning tasks leads to the
increased computational resources required onboard. To address the problem of
memory consumption in long-term operation, we develop a novel real-time SLAM
algorithm, MeSLAM, that is based on neural field implicit map representation.
It combines the proposed global mapping strategy, including neural networks
distribution and region tracking, with an external odometry system. As a
result, the algorithm is able to efficiently train multiple networks
representing different map regions and track poses accurately in large-scale
environments. Experimental results show that the accuracy of the proposed
approach is comparable to the state-of-the-art methods (on average, 6.6 cm on
TUM RGB-D sequences) and outperforms the baseline, iMAP$^*$. Moreover, the
proposed SLAM approach provides the most compact-sized maps without details
distortion (1.9 MB to store 57 m$^3$) among the state-of-the-art SLAM
approaches.
