Robots rely on visual relocalization to estimate their pose from camera images when they lose track. One of the challenges in visual relocalization is repetitive structures in the operation environment of the robot. This calls for probabilistic methods that support multiple hypotheses for robot's pose. We propose such a probabilistic method to predict the posterior distribution of camera poses given an observed image. Our proposed training strategy results in a generative model of camera poses given an image, which can be used to draw samples from the pose posterior distribution. Our method is streamlined and well-founded in theory and outperforms existing methods on localization in presence of ambiguities.

翻译：当机器人失去跟踪时，它们依赖视觉重定位来根据相机图像估计自身姿态。视觉重定位中的一个挑战是机器人操作环境中的重复结构。这需要能够支持机器人姿态多假设的概率方法。我们提出了一种概率方法来预测给定观测图像时相机姿态的后验分布。我们提出的训练策略产生了一个给定图像时相机姿态的生成模型，该模型可用于从姿态后验分布中抽取样本。我们的方法流程简洁、理论基础坚实，并且在存在模糊性的定位任务中优于现有方法。