Robot-assisted Backscatter Localization for IoT Applications

TL;DR

This paper introduces Rover, an indoor localization system that uses a robot with inertial sensors to localize backscatter tags without prior site knowledge, enabling scalable IoT applications.

Contribution

Rover is the first system to localize multiple backscatter tags without site-specific calibration, combining WiFi signals and inertial sensors in a joint optimization framework.

Findings

Achieves 39.3 cm accuracy for robot localization

Achieves 74.6 cm accuracy for backscatter tags

Operates without prior site knowledge

Abstract

Recent years have witnessed the rapid proliferation of backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such backscatter tags is crucial for IoT-based smart applications. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, which is laborious for deployment. To empower universal localization service, this paper presents Rover, an indoor localization system that localizes multiple backscatter tags without any start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing measurements from backscattered WiFi signals and inertial sensors to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues including interference among multiple tags, real-time processing, as well as the data marginalization problem in dealing with degenerated motions. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.