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.

翻译：自动驾驶车辆需求的日益增长，促使导航系统需具备在恶劣天气条件下有效运行的鲁棒性。视觉里程计作为导航系统中的关键技术，通过车载摄像头输入实现车辆位置与运动的估计。然而，大雨、降雪或雾等恶劣天气会显著影响视觉里程计的精度。本文评估了多种视觉里程计方法，包括我们基于DROID-SLAM的启发式方法。具体而言，这些算法在晴朗与雨天城市驾驶数据上进行测试，以评估其鲁棒性。我们构建了一个包含不同城市多种雨况的数据集，涵盖牛津Robotcar数据集、慕尼黑4Seasons数据集以及新加坡内部采集数据集。我们采用绝对轨迹误差（ATE）指标，评估了单目与立体相机配置下的多种视觉里程计算法。研究结果表明，在评估的方法中，单目配置的深度与光流视觉里程计（DF-VO）算法在短距离（<500米）场景下表现最优，而立体配置下我们提出的基于DROID-SLAM的启发式方法在长期定位中效果相对较好。两种VO算法均表明，雨天定位需要更鲁棒的基于传感器融合的方法。