LED状態予測を補助タスクに用いた視覚的ロボット自己位置推定の自己教師あり学習
Self-Supervised Learning of Visual Robot Localization Using LED State Prediction as a Pretext Task
ロボットに搭載したLEDの状態予測を補助タスクとして使うことで、位置ラベル付き画像が少数でも高精度な視覚的自己位置推定を学習できる自己教師あり手法を提案し、ナノドローンでの追従実験で有効性を示した。
著者: Mirko Nava, Nicholas Carlotti, Luca Crupi, Daniele Palossi, Alessandro Giusti
分類: cs.RO
原文アブストラクト
We propose a novel self-supervised approach for learning to visually localize robots equipped with controllable LEDs. We rely on a few training samples labeled with position ground truth and many training samples in which only the LED state is known, whose collection is cheap. We show that using LED state prediction as a pretext task significantly helps to learn the visual localization end task. The resulting model does not require knowledge of LED states during inference. We instantiate the approach to visual relative localization of nano-quadrotors: experimental results show that using our pretext task significantly improves localization accuracy (from 68.3% to 76.2%) and outperforms alternative strategies, such as a supervised baseline, model pre-training, and an autoencoding pretext task. We deploy our model aboard a 27-g Crazyflie nano-drone, running at 21 fps, in a position-tracking task of a peer nano-drone. Our approach, relying on position labels for only 300 images, yields a mean tracking error of 4.2 cm versus 11.9 cm of a supervised baseline model trained without our pretext task. Videos and code of the proposed approach are available at https://github.com/idsia-robotics/leds-as-pretext
