  Recent years have witnessed the rapid proliferation of low-power backscatter
technologies that realize the ubiquitous and long-term connectivity to empower
smart cities and smart homes. Localizing such low-power backscatter tags is
crucial for IoT-based smart services. However, current backscatter localization
systems require prior knowledge of the site, either a map or landmarks with
known positions, increasing the deployment cost. To empower universal
localization service, this paper presents Rover, an indoor localization system
that simultaneously localizes multiple backscatter tags with zero start-up cost
using a robot equipped with inertial sensors. Rover runs in a joint
optimization framework, fusing WiFi-based positioning measurements with
inertial measurements to simultaneously estimate the locations of both the
robot and the connected tags. Our design addresses practical issues such as the
interference among multiple tags and the real-time processing for solving the
SLAM problem. 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.
