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.

翻译：大多数6自由度定位和SLAM系统使用静态地标，但忽略动态物体，因为它们无法有效融入典型处理流程。在已纳入动态物体的方法中，典型做法试图对这些物体进行相对复杂的识别和定位，从而限制了其鲁棒性或通用性。在本研究中，我们提出了一种折中方案，并在自动驾驶汽车场景中进行了验证，即利用动态车辆在逐帧PnP-RANSAC定位管道中提供有限的位姿约束信息。我们通过运动模型对初始位姿估计进行优化，并提出一种预测未来位姿估计质量的方法，该方法根据自动驾驶汽车的运动是否受环境中动态车辆相对帧间位置约束来触发。我们的方法检测并识别合适的动态车辆，以定义这些位姿约束来修改位姿滤波器，从而在从$0.25m$到$5m$的定位容差范围内，相比最先进的单图像PnP基线方法及其原始位姿滤波，提高了召回率。在福特AV数据集上，我们的约束检测系统在大约$35\%$的时间内处于激活状态，且当约束检测激活时，定位性能显著提升。