6DOF camera relocalization is an important component of autonomous driving and navigation. Deep learning has recently emerged as a promising technique to tackle this problem. In this paper, we present a novel relative geometry-aware Siamese neural network to enhance the performance of deep learning-based methods through explicitly exploiting the relative geometry constraints between images. We perform multi-task learning and predict the absolute and relative poses simultaneously. We regularize the shared-weight twin networks in both the pose and feature domains to ensure that the estimated poses are globally as well as locally correct. We employ metric learning and design a novel adaptive metric distance loss to learn a feature that is capable of distinguishing poses of visually similar images from different locations. We evaluate the proposed method on public indoor and outdoor benchmarks and the experimental results demonstrate that our method can significantly improve localization performance. Furthermore, extensive ablation evaluations are conducted to demonstrate the effectiveness of different terms of the loss function.
翻译:6DOF 相机重新定位是自主驾驶和导航的一个重要组成部分。 深层次学习最近成为解决这一问题的一个很有希望的方法。 在本文中,我们展示了一个新的相对几何觉觉悟的暹粒神经网络,通过明确利用图像之间的相对几何限制来提高深层次学习方法的性能。 我们进行多任务学习,同时预测绝对和相对的成份。 我们将面貌和特征领域的共享重量双胞胎网络正规化,以确保估计的成份在全球和地方上都正确。 我们采用计量学习,设计一种新的适应性公径距离损失,以学习能够区分不同地点相近图像的特征。 我们评估了公共室内和室外基准的拟议方法和实验结果,表明我们的方法可以显著改善本地化绩效。 此外,还进行了广泛的缩缩评价,以显示损失功能不同条件的有效性。