Most 6-DoF localization and SLAM systems use static landmarks but ignore
dynamic objects because they cannot be usefully incorporated into a typical
pipeline. Where dynamic objects have been incorporated, typical approaches have
attempted relatively sophisticated identification and localization of these
objects, limiting their robustness or general utility. In this research, we
propose a middle ground, demonstrated in the context of autonomous vehicles,
using dynamic vehicles to provide limited pose constraint information in a
6-DoF frame-by-frame PnP-RANSAC localization pipeline. We refine initial pose
estimates with a motion model and propose a method for calculating the
predicted quality of future pose estimates, triggered based on whether or not
the autonomous vehicle's motion is constrained by the relative frame-to-frame
location of dynamic vehicles in the environment. Our approach detects and
identifies suitable dynamic vehicles to define these pose constraints to modify
a pose filter, resulting in improved recall across a range of localization
tolerances from $0.25m$ to $5m$, compared to a state-of-the-art baseline single
image PnP method and its vanilla pose filtering. Our constraint detection
system is active for approximately $35\%$ of the time on the Ford AV dataset
and localization is particularly improved when the constraint detection is
active.

该研究提出一种使用动态车辆提供有限位姿约束信息的 6-DoF 逐帧 PnP-RANSAC 定位管道方法，可改善单图像 PnP 方法和其基准位姿过滤的记忆率，并在车辆移动被相对帧 - 帧位置约束的情况下触发计算未来位姿估计质量的方法。

锁定：利用动态车辆约束来改善视觉定位

Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to  Improve Visual Localization

Machine learning techniques, namely convolutional neural networks (CNN) and
regression forests, have recently shown great promise in performing 6-DoF
localization of monocular images. However, in most cases image-sequences,
rather only single images, are readily available. To this extent, none of the
proposed learning-based approaches exploit the valuable constraint of temporal
smoothness, often leading to situations where the per-frame error is larger
than the camera motion. In this paper we propose a recurrent model for
performing 6-DoF localization of video-clips. We find that, even by considering
only short sequences (20 frames), the pose estimates are smoothed and the
localization error can be drastically reduced. Finally, we consider means of
obtaining probabilistic pose estimates from our model. We evaluate our method
on openly-available real-world autonomous driving and indoor localization
datasets.

本文介绍了一种使用循环模型进行短视频聚合并实现相机定位的方法。通过考虑短视频的时间平滑性，不仅可以使位姿估计更加平滑，还可以显著降低相机定位误差，并提出了一种从该模型中获得概率位姿估计的方法。我们在实际的自动驾驶和室内定位数据集上进行了评估。