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Nano-sized unmanned aerial vehicles (UAVs) are well-fit for indoorapplications and for close proximity to humans. To enable autonomy, thenano-UAV must be able to self-localize in its operating environment. This is aparticularly-challenging task due to the limited sensing and compute resourceson board. This work presents an online and onboard approach for localization infloor plans annotated with semantic information. Unlike sensor-based maps,floor plans are readily-available, and do not increase the cost and time ofdeployment. To overcome the difficulty of localizing in sparse maps, theproposed approach fuses geometric information from miniaturized time-of-flightsensors and semantic cues. The semantic information is extracted from images bydeploying a state-of-the-art object detection model on a high-performancemulti-core microcontroller onboard the drone, consuming only 2.5mJ per frameand executing in 38ms. In our evaluation, we globally localize in a real-worldoffice environment, achieving 90% success rate. We also release an open-sourceimplementation of our work.

Fully Onboard Low-Power Localization with Semantic Sensor Fusion on a Nano-UAV using Floor Plans