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The advances in agile micro aerial vehicles (MAVs) have shown great potentialin replacing humans for labor-intensive or dangerous indoor investigation, suchas warehouse management and fire rescue. However, the design of a stateestimation system that enables autonomous flight poses fundamental challengesin such dim or smoky environments. Current dominated computer-vision basedsolutions only work in well-lighted texture-rich environments. This paperaddresses the challenge by proposing Marvel, an RF backscatter-based stateestimation system with online initialization and calibration. Marvel isnonintrusive to commercial MAVs by attaching backscatter tags to their landinggears without internal hardware modifications, and works in a plug-and-playfashion with an automatic initialization module. Marvel is enabled by three newdesigns, a backscatter-based pose sensing module, an online initialization andcalibration module, and a backscatter-inertial super-accuracy state estimationalgorithm. We demonstrate our design by programming a commercial MAV toautonomously fly in different trajectories. The results show that Marvelsupports navigation within a range of 50 m or through three concrete walls,with an accuracy of 34 cm for localization and 4.99 degrees for orientationestimation. We further demonstrate our online initialization and calibration bycomparing to the perfect initial parameter measurements from burdensome manualoperations.

LoRa Backscatter Assisted State Estimator for Micro Aerial Vehicles with Online Initialization