  Simultaneous localization and mapping (SLAM) is a crucial functionality for
exploration robots and virtual/augmented reality (VR/AR) devices. However, some
of such devices with limited resources cannot afford the computational or
memory cost to run full SLAM algorithms. We propose a general client-server
SLAM optimization framework that achieves accurate real-time state estimation
on the device with low requirements of on-board resources. The resource-limited
device (the client) only works on a small part of the map, and the rest of the
map is processed by the server. By sending the summarized information of the
rest of map to the client, the on-device state estimation is more accurate.
Further improvement of accuracy is achieved in the presence of on-device early
loop closures, which enables reloading useful variables from the server to the
client. Experimental results from both synthetic and real-world datasets
demonstrate that the proposed optimization framework achieves accurate
estimation in real-time with limited computation and memory budget of the
device.
