Edge-Assisted Multi-Robot Visual-Inertial SLAM with Efficient Communication

TL;DR

This paper presents an edge-assisted multi-robot SLAM system that reduces communication bandwidth and computational load while maintaining high accuracy through a layered architecture and efficient data encoding.

Contribution

It introduces a lightweight optical flow tracking method and a layered robot-edge-cloud architecture for real-time collaborative SLAM with optimized data transmission.

Findings

Achieves lower data volume transmission compared to existing methods.

Maintains or improves positioning accuracy under low computational load.

Demonstrates effectiveness on EuRoC dataset.

Abstract

The integration of cloud computing and edge computing is an effective way to achieve global consistent and real-time multi-robot Simultaneous Localization and Mapping (SLAM). Cloud computing effectively solves the problem of limited computing, communication and storage capacity of terminal equipment. However, limited bandwidth and extremely long communication links between terminal devices and the cloud result in serious performance degradation of multi-robot SLAM systems. To reduce the computational cost of feature tracking and improve the real-time performance of the robot, a lightweight SLAM method of optical flow tracking based on pyramid IMU prediction is proposed. On this basis, a centralized multi-robot SLAM system based on a robot-edge-cloud layered architecture is proposed to realize real-time collaborative SLAM. It avoids the problems of limited on-board computing resources and low execution efficiency of single robot. In this framework, only the feature points and keyframe descriptors are transmitted and lossless encoding and compression are carried out to realize real-time remote information transmission with limited bandwidth resources. This design reduces the actual bandwidth occupied in the process of data transmission, and does not cause the loss of SLAM accuracy caused by data compression. Through experimental verification on the EuRoC dataset, compared with the current most advanced local feature compression method, our method can achieve lower data volume feature transmission, and compared with the current advanced centralized multi-robot SLAM scheme, it can achieve the same or better positioning accuracy under low computational load.