  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.
