Distributed Client-Server Optimization for SLAM with Limited On-Device Resources
This addresses the challenge of running SLAM on exploration robots and VR/AR devices with limited resources, representing an incremental improvement through a hybrid approach.
The paper tackles the problem of enabling accurate real-time SLAM on resource-limited devices by proposing a distributed client-server optimization framework, which achieves accurate state estimation with low on-device computational and memory requirements.
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.