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.
Abstract (translated)
云计算与边缘计算的融合是实现全球一致性和实时多机器人同时定位与地图构建（SLAM）的有效途径。云计算有效地解决了终端设备在计算、通信和存储能力方面的局限性问题。然而，终端设备与云端之间的有限带宽以及极长的通信链路导致了多机器人SLAM系统性能严重下降。为了降低特征跟踪的计算成本并提高机器人的实时性能，提出了一种基于金字塔惯性测量单元（IMU）预测的光流跟踪轻量级SLAM方法。在此基础上，提出了一个基于机器人-边缘-云分层架构的集中式多机器人SLAM系统，以实现实时协作SLAM。这种方法避免了车载计算资源有限和单个机器人执行效率低的问题。在该框架中，仅传输特征点和关键帧描述符，并进行无损编码压缩，从而实现在有限带宽资源下的实时远程信息传输。这一设计减少了数据传输过程中的实际带宽占用，并且不会因数据压缩而导致SLAM精度的损失。通过在EuRoC数据集上的实验验证表明，与目前最先进的局部特征压缩方法相比，我们的方法可以实现更小的数据量特征传输；与当前先进的集中式多机器人SLAM方案相比，在低计算负载下可以达到相同或更好的定位精度。
URL
https://arxiv.org/abs/2603.11085
