ナノUAVにおける間取り図を用いたセマンティックセンサフュージョンによる完全オンボード低電力自己位置推定
Fully Onboard Low-Power Localization with Semantic Sensor Fusion on a Nano-UAV using Floor Plans
ナノUAVが間取り図とセマンティック情報を融合し、オンボードで低電力に自己位置推定を行う手法を提案し、実オフィス環境で90%の成功率を達成した。
著者: Nicky Zimmerman, Hanna Müller, Michele Magno, Luca Benini
分類: cs.RO
原文アブストラクト
Nano-sized unmanned aerial vehicles (UAVs) are well-fit for indoor applications and for close proximity to humans. To enable autonomy, the nano-UAV must be able to self-localize in its operating environment. This is a particularly-challenging task due to the limited sensing and compute resources on board. This work presents an online and onboard approach for localization in floor plans annotated with semantic information. Unlike sensor-based maps, floor plans are readily-available, and do not increase the cost and time of deployment. To overcome the difficulty of localizing in sparse maps, the proposed approach fuses geometric information from miniaturized time-of-flight sensors and semantic cues. The semantic information is extracted from images by deploying a state-of-the-art object detection model on a high-performance multi-core microcontroller onboard the drone, consuming only 2.5mJ per frame and executing in 38ms. In our evaluation, we globally localize in a real-world office environment, achieving 90% success rate. We also release an open-source implementation of our work.
