Accurate maps are a prerequisite for virtually all autonomous vehicle tasks. Most state-of-the-art maps assume a static world, and therefore dynamic objects are filtered out of the measurements. However, this division ignores movable but non-moving, i.e. semi-static, objects, which are usually recorded in the map and treated as static objects, violating the static world assumption, causing error in the localization. In this paper, we present a method for modeling moving and movable objects for matching the map and the measurements consistently. This reduces the error resulting from inconsistent categorization and treatment of non-static measurements. A semantic segmentation network is used to categorize the measurements into static and semi-static classes, and a background subtraction-based filtering method is used to remove dynamic measurements. Experimental comparison against a state-of-the-art baseline solution using real-world data from Oxford Radar RobotCar data set shows that consistent assumptions over dynamics increase localization accuracy.

翻译：精确地图几乎是所有自主驾驶任务的前提。最先进的地图通常假设静态世界，因此动态对象被从测量中滤除。然而，这种划分忽略了可移动但未移动的物体（即半静态物体），这些物体通常被记录在地图中并作为静态物体处理，从而违反了静态世界假设，导致定位误差。本文提出一种对移动物体和可移动物体进行建模的方法，以实现地图与测量的一致匹配。这减少了因非静态测量分类不一致和处理方式不同而产生的误差。通过语义分割网络将测量分类为静态和半静态类别，并采用基于背景减除的滤波方法去除动态测量。利用牛津机器人雷达数据集的实际数据与最先进的基准方法进行实验对比，结果表明动态一致性假设能够提高定位精度。