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

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.

Figures (15)

• Figure 1: Multi-robot Collaborative SLAM communication architecture.
• Figure 2: System overview. Each robot is equipped with a Visual-Inertial sensor suite. After analyzing the sensor information, the compressed data is transmitted to a fixed edge server. The edge server can conduct location and map construction in local scenes by running the VIO in real-time and independently. The communication interface is used to exchange data (keyframe local binary features and mappoints) between the edge server and the cloud. The cloud can run computationally expensive, global, and non-real-time tasks: redundant keyframe culling, loop closure, and global map fusion.
• Figure 3: IMU pre-integration diagram. Integrating all IMU information between the $k\rm {th}$ frame and the $(k+1) \rm {th}$ frame, the position, velocity and rotation (PVQ) of the $(k+1) \rm {th}$ frame can be obtained as the initial value of the visual estimation.
• Figure 4: The choice of encoding mode. The decision of keyframes depends on the number of points tracked by the IMU-assisted LK optical flow and the time interval from the previous keyframe.
• Figure 5: Visual-Inertial joint optimization factor graph.