  The increasing demand for autonomous vehicles has created a need for robust
navigation systems that can also operate effectively in adverse weather
conditions. Visual odometry is a technique used in these navigation systems,
enabling the estimation of vehicle position and motion using input from onboard
cameras. However, visual odometry accuracy can be significantly impacted in
challenging weather conditions, such as heavy rain, snow, or fog. In this
paper, we evaluate a range of visual odometry methods, including our DROID-SLAM
based heuristic approach. Specifically, these algorithms are tested on both
clear and rainy weather urban driving data to evaluate their robustness. We
compiled a dataset comprising of a range of rainy weather conditions from
different cities. This includes, the Oxford Robotcar dataset from Oxford, the
4Seasons dataset from Munich and an internal dataset collected in Singapore. We
evaluated different visual odometry algorithms for both monocular and stereo
camera setups using the Absolute Trajectory Error (ATE). From the range of
approaches evaluated, our findings suggest that the Depth and Flow for Visual
Odometry (DF-VO) algorithm with monocular setup performed the best for short
range distances (< 500m) and our proposed DROID-SLAM based heuristic approach
for the stereo setup performed relatively well for long-term localization. Both
VO algorithms suggested a need for a more robust sensor fusion based approach
for localization in rain.
