Cloud-Based Autonomous Indoor Navigation: A Case Study

TL;DR

This paper presents a cloud-enabled autonomous indoor navigation system that improves map accuracy and reduces mapping time by offloading computation to the cloud, demonstrated through a corridor mapping case study.

Contribution

It introduces a novel cloud-based architecture for autonomous indoor navigation, integrating map generation, robot coordination, and online monitoring.

Findings

Autonomous mode yields more accurate maps.

Cloud offloading significantly reduces mapping time.

System enables real-time monitoring and emergency control.

Abstract

In this case study, we design, integrate and implement a cloud-enabled autonomous robotic navigation system. The system has the following features: map generation and robot coordination via cloud service and video streaming to allow online monitoring and control in case of emergency. The system has been tested to generate a map for a long corridor using two modes: manual and autonomous. The autonomous mode has shown more accurate map. In addition, the field experiments confirm the benefit of offloading the heavy computation to the cloud by significantly shortening the time required to build the map.