用自监督方法让无人机视觉定位模型快速适应真实环境，仅需10分钟实测数据
Self-supervised Domain Adaptation for Visual 3D Pose Estimation of Nano-drone Racing Gates by Enforcing Geometric Consistency
- 利用无人机自身里程计自监督标注真实图像序列，构建几何一致性损失
- 位置误差低至26/28/10厘米，方向误差13度，性能提升40%以上
- 适合部署在微型无人机上，推理速度达33帧每秒，实用性强
本文研究如何通过视觉估计纳米无人机前方竞速门的相对位姿，使用在仿真数据上预训练的卷积神经网络进行位姿回归。由于仿真到现实的差距，预训练模型在真实场景中表现不佳，需进行域适应。提出一种仅需无人机任意轨迹飞行时采集的真实图像序列的无监督域适应（UDA）方法，通过机载传感器测量的里程计对序列进行自监督标注。在此数据集上，采用状态一致性损失约束不同时刻获取的图像预测位姿与无人机里程计保持一致。实验表明，该方法优于其他先进无监督域适应方法，在位置上的均方绝对误差分别为x=26cm、y=28cm、z=10cm，方向误差ψ=13°，相比基线在位置上提升40%，方向上提升37%。仅需10分钟真实飞行数据即可显著提升性能，部署于Crazyflie 2.1无刷纳米无人机时，推理时间为30.4ms（33 fps）。
原文摘要 · Abstract (English)
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 ($ψ$=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.
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