This paper presents a localization algorithm for autonomous urban vehicles under rain weather conditions. In adverse weather, human drivers anticipate the location of the ego-vehicle based on the control inputs they provide and surrounding road contextual information. Similarly, in our approach for localization in rain weather, we use visual data, along with a global reference path and vehicle motion model for anticipating and better estimating the pose of the ego-vehicle in each frame. The global reference path contains useful road contextual information such as the angle of turn which can be potentially used to improve the localization accuracy especially when sensors are compromised. We experimented on the Oxford Robotcar Dataset and our internal dataset from Singapore to validate our localization algorithm in both clear and rain weather conditions. Our method improves localization accuracy by 50.83% in rain weather and 34.32% in clear weather when compared to baseline algorithms.

翻译：本文提出了一种适用于雨天条件下城市自动驾驶车辆的定位算法。在恶劣天气中，人类驾驶员会根据自身施加的控制输入及周围道路环境信息，预判本车位置。类似地，在雨天定位方案中，我们利用视觉数据、全局参考路径与车辆运动模型，对每一帧中本车的位姿进行预判和更优估计。全局参考路径包含转向角度等有用的道路环境信息，可在传感器性能受限时显著提升定位精度。我们通过牛津Robotcar数据集及新加坡本地数据集，在晴朗与雨天两种条件下验证了该定位算法。与基准算法相比，本方法在雨天环境中定位精度提升50.83%，在晴朗环境中提升34.32%。