面向雨天自动驾驶的视觉里程计方法评估
Evaluating Visual Odometry Methods for Autonomous Driving in Rain
AI总结：
本文评估多种视觉里程计方法在雨天城市驾驶中的鲁棒性，发现单目DF-VO在短距离最佳，而基于DROID-SLAM的双目启发式方法在长期定位表现较好，并指出雨天定位需更鲁棒的传感器融合方案。
摘要：
自动驾驶汽车日益增长的需求催生了对鲁棒导航系统的需要，这类系统还必须在恶劣天气条件下有效运行。视觉里程计是这些导航系统中使用的一种技术，能够利用车载摄像头的输入来估计车辆位置和运动。然而，在诸如大雨、大雪或大雾等具有挑战性的天气条件下，视觉里程计的精度可能受到显著影响。在本文中，我们评估了一系列视觉里程计方法，包括我们基于DROID-SLAM的启发式方法。具体而言，这些算法在晴天和雨天城市驾驶数据上均进行了测试，以评估其鲁棒性。我们编制了一个数据集，包含来自不同城市的一系列雨天天气条件。该数据集包括来自牛津的Oxford Robotcar数据集、来自慕尼黑的4Seasons数据集以及在新加坡采集的内部数据集。我们使用绝对轨迹误差（ATE）评估了单目和双目相机配置下的不同视觉里程计算法。在所评估的一系列方法中，我们的研究结果表明，采用单目配置的Depth and Flow for Visual Odometry（DF-VO）算法在短距离（< 500m）下表现最佳，而我们提出的基于DROID-SLAM的启发式方法在双目配置下对长期定位表现相对较好。两种VO算法均表明，在雨天进行定位需要一种更鲁棒的基于传感器融合的方法。
英文摘要：
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.
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