Deep learning based camera pose estimation from monocular camera images has seen a recent uptake in Visual SLAM research. Even though such pose estimation approaches have excellent results in small confined areas like offices and apartment buildings, they tend to do poorly when applied to larger areas like outdoor settings, mainly because of the scarcity of distinctive features. We propose GODSAC* as a camera pose estimation approach that augments pose predictions from a trained neural network with noisy odometry data through the optimization of a pose graph. GODSAC* outperforms the state-of-the-art approaches in pose estimation accuracy, as we demonstrate in our experiments.

中文翻译：

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GODSAC *：针对机器人进行重新定位的图形优化DSAC *

从单眼相机图像中基于深度学习的相机姿态估计已在Visual SLAM研究中得到了广泛应用。尽管这种姿势估计方法在诸如办公室和公寓楼之类的狭窄密闭区域中具有出色的结果，但是当应用于较大的区域（如室外环境）时，它们的效果往往很差，这主要是由于缺乏鲜明的特征。我们建议将GODSAC *作为一种相机姿态估计方法，通过优化姿态图来增强带有噪声里程表数据的，经过训练的神经网络的姿态预测。正如我们在实验中所展示的，GODSAC *在姿态估计准确性方面优于最新方法。