论文标题
在天气条件下基于相机和基于3D激光雷达的循环封闭的比较
Comparison of camera-based and 3D LiDAR-based loop closures across weather conditions
论文作者
论文摘要
基于相机图像的循环封闭可为基准测试数据集提供出色的效果，但可能在现实世界中的不利天气条件中挣扎，例如直达阳光，雨，雾或夜间黑暗。在汽车应用程序中，感官设置包括3D激光雷达，可提供相机互补的信息。介绍的文章重点是评估基于摄像头，基于激光雷达的基于摄像头和基于摄像头的环路封闭，该环路封闭使用了类似的处理管道，该处理管线在不同的天气条件下使用新近可用的USYD数据集组成的神经网络组成。在50周内在不同天气条件下在相同轨迹上进行的实验证明，可以使用16线3D激光雷达来补充基于图像的环路闭合以提高环路闭合性能。这证明需要对使用多感官设置进行的循环封闭进行更多研究。
Loop closure based on camera images provides excellent results on benchmarking datasets, but might struggle in real-world adverse weather conditions like direct sun, rain, fog, or just darkness at night. In automotive applications, the sensory setups include 3D LiDARs that provide information complementary to cameras. The presented article focuses on the evaluation of camera-based, LiDAR-based, and joint camera-LiDAR-based loop closures applying a similar processing pipeline consisting of a neural network under varying weather conditions using the newly available USyd dataset. The experiments performed on the same trajectories in diverse weather conditions over 50 weeks prove that a 16-line 3D LiDAR can be used to supplement image-based loop closure to increase loop closure performance. This proves that there is a need for more research into loop closures performed with multi-sensory setups.
