Visual localization techniques often comprise a hierarchical localization pipeline, with a visual place recognition module used as a coarse localizer to initialize a pose refinement stage. While improving the pose refinement step has been the focus of much recent research, most work on the coarse localization stage has focused on improvements like increased invariance to appearance change, without improving what can be loose error tolerances. In this letter, we propose two methods which adapt image retrieval techniques used for visual place recognition to the Bayesian state estimation formulation for localization. We demonstrate significant improvements to the localization accuracy of the coarse localization stage using our methods, whilst retaining state-of-the-art performance under severe appearance change. Using extensive experimentation on the Oxford RobotCar dataset, results show that our approach outperforms comparable state-of-the-art methods in terms of precision-recall performance for localizing image sequences. In addition, our proposed methods provides the flexibility to contextually scale localization latency in order to achieve these improvements. The improved initial localization estimate opens up the possibility of both improved overall localization performance and modified pose refinement techniques that leverage this improved spatial prior.

中文翻译：

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分层定位的概率视觉位置识别

视觉定位技术通常包括分层的定位流水线，其中视觉位置识别模块用作粗略的定位器以初始化姿势优化阶段。尽管改进姿势细化步骤一直是许多最新研究的重点，但是在粗略定位阶段的大多数工作都集中在改进诸如外观变化不变性增加等方面，而没有改善可能的宽松误差容限。在这封信中，我们提出了两种方法，这些方法将用于视觉位置识别的图像检索技术适配于用于定位的贝叶斯状态估计公式。我们证明了使用我们的方法可以对粗定位阶段的定位精度进行重大改进，同时在出现严重外观变化时仍能保持最先进的性能。在牛津RobotCar数据集上进行了广泛的实验，结果表明，在定位图像序列的精确调用性能方面，我们的方法优于可比的最新技术。另外，我们提出的方法提供了根据上下文缩放本地化延迟的灵活性，以实现这些改进。改进的初始定位估计为改进整体定位性能和利用这种改进的空间先验的改进的姿态优化技术提供了可能性。我们提出的方法提供了根据上下文缩放本地化延迟的灵活性，以实现这些改进。改进的初始定位估计为改进整体定位性能和利用这种改进的空间先验的改进的姿态优化技术提供了可能性。我们提出的方法提供了根据上下文缩放本地化延迟的灵活性，以实现这些改进。改进的初始定位估计值为改进总体定位性能和利用这种改进的空间先验的改进的姿态优化技术提供了可能性。