Uncertainty-Aware Semi-Supervised Semantic Key Point Detection via Bundle Adjustment

Visual relative localization is widely used in multi-robot systems. While semantic key points offer a promising solution for 6DoF pose estimation, manual data labeling for network training remains unavoidable. In this paper, we introduce a novel method that jointly estimates the semantic key point detection model and 6DoF camera pose. Our key idea is to leverage the 3D-2D projection to produce pseudo labels for detection model training while taking the key point predictions as landmarks for 6DoF camera pose estimation. Compared with state-of-the-art works, our method eliminates the need for calibration and time synchronization of multi-camera systems, requiring only a handful of manually labeled data, which significantly improves the training efficiency. The experiment validates the effectiveness and practicality of our method in public datasets and real-world robotic applications. Code and data are made available<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>.