幾何的整合性を強制するナノドローンレースゲートの視覚的3D姿勢推定のための自己教師ありドメイン適応
Self-supervised Domain Adaptation for Visual 3D Pose Estimation of Nano-drone Racing Gates by Enforcing Geometric Consistency
シミュレーションデータで事前学習したCNNを実環境に適応させるため、ドローンのオドメトリを用いた自己教師ありのドメイン適応手法を提案。実画像シーケンスのみで姿勢予測の整合性を保証し、位置誤差40%、姿勢誤差37%改善。
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著者: Nicholas Carlotti, Michele Antonazzi, Elia Cereda, Mirko Nava, Nicola Basilico, Daniele Palossi, Alessandro Giusti
分類: cs.RO
原文アブストラクト
We consider the task of visually estimating the relative pose of a drone racing gate in front of a nano-quadrotor, using a convolutional neural network pre-trained on simulated data to regress the gate's pose. Due to the sim-to-real gap, the pre-trained model underperforms in the real world and must be adapted to the target domain. We propose an unsupervised domain adaptation (UDA) approach using only real image sequences collected by the drone flying an arbitrary trajectory in front of a gate; sequences are annotated in a self-supervised fashion with the drone's odometry as measured by its onboard sensors. On this dataset, a state consistency loss enforces that two images acquired at different times yield pose predictions that are consistent with the drone's odometry. Results indicate that our approach outperforms other SoA UDA approaches, has a low mean absolute error in position (x=26, y=28, z=10 cm) and orientation ($\psi$=13${^{\circ}}$), an improvement of 40% in position and 37% in orientation over a baseline. The approach's effectiveness is appreciable with as few as 10 minutes of real-world flight data and yields models with an inference time of 30.4ms (33 fps) when deployed aboard the Crazyflie 2.1 Brushless nano-drone.
