GODSAC *：用于机器人重新定位的图形优化DSAC *
GODSAC*: Graph Optimized DSAC* for Robot Relocalization
摘要：
基于深度学习的单目相机图像的相机姿态估计最近在Visual SLAM研究中得到了广泛应用。尽管这种姿态估计方法在办公室和公寓楼等小的封闭区域中具有良好的效果，但当应用于户外环境等较大区域时，它们往往表现不佳，这主要是因为缺乏独特的特征。我们提出GODSAC* 作为一种相机姿态估计方法，通过优化姿态图来增强来自具有噪声里程计数据的训练神经网络的姿态预测。GODSAC* 在姿态估计精度方面优于最先进的方法，正如我们在实验中所证明的那样。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
