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 DROIDSLAM 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). Our evaluation suggests that the Depth and Flow for Visual Odometry (DF-VO) algorithm with monocular setup worked well 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 algorithms performed consistently well across all rain conditions.

翻译：日益增长的自驾车需求催生了能够在恶劣天气条件下有效运行的鲁棒导航系统。视觉里程计是这些导航系统中使用的技术，可通过车载摄像头输入实现车辆位置和运动的估计。然而，在暴雨、降雪或浓雾等恶劣天气条件下，视觉里程计精度会显著下降。本文评估了一系列视觉里程计方法，包括基于DROID-SLAM的启发式方法。具体而言，在晴朗和雨天的城市驾驶数据上测试这些算法以评估其鲁棒性。我们从不同城市收集了涵盖多种雨天条件的数据集，包括牛津Robotcar数据集（牛津）、慕尼黑4Seasons数据集以及在新加坡收集的内部数据集。采用绝对轨迹误差（ATE）评估了单目和立体相机配置下的不同视觉里程计算法。评估表明：单目配置下，基于深度与光流的视觉里程计（DF-VO）算法在短距离（<500米）场景中表现优异；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表现相对较好。两种算法在所有降雨条件下均能保持稳定的性能。