Distributed Client-Server Optimization for SLAM with Limited On-Device Resources

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.

Authors

• Yetong Zhang
• Ming Hsiao
• Yipu Zhao
• Jing Dong 0002
• Jakob J. Engel

Keywords

• Performance evaluation
• Simultaneous localization and mapping
• Uncertainty
• Systematics
• Refining
• Real-time systems
• Servers
• Limited Resources
• Real-world Datasets
• Optimization Framework
• Real-time Estimation
• Loop Closure
• Accurate Estimation
• Factorization
• Optimal Time
• Estimation Results
• New Variables
• Multi-core
• Local Map
• Front End
• Memory Consumption
• History Variables
• Communication Delay
• Uncertainty Information
• Inference Problem
• Sparse Method
• Factor Graph
• Graph Size
• Message Size
• Early Closure
• Local Graph
• Lightweight Devices
• Global Optimization