  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.
