MCVO：一种适用于任意排列多相机的通用视觉里程计
MCVO: A Generic Visual Odometry for Arbitrarily Arranged Multi-Cameras
AI总结：
针对多相机任意排列导致的尺度估计难题，提出MCVO视觉里程计，利用学习型特征跟踪、刚性约束初始化和后端多相机特征融合，实现高精度鲁棒位姿估计，在KITTI-360等数据集上优于现有系统。
摘要：
使多相机视觉SLAM系统更易于设置并对环境更具鲁棒性，对视觉机器人而言颇具吸引力。现有的单目和双目视觉SLAM系统具有狭窄的感知视场（FoV），导致在无纹理环境中精度下降且鲁棒性受限。因此，多相机SLAM系统正受到关注，因为它们能以更宽的视场提供冗余。然而，多相机通常的任意放置和朝向使得位姿尺度估计和系统更新具有挑战性。为解决这些问题，我们提出了一种针对刚性捆绑的任意排列多相机的鲁棒视觉里程计系统，即MCVO，它能在相机排列上具有高灵活性的同时实现度量尺度状态估计。具体而言，我们首先设计了一个基于学习的特征跟踪框架，将多个视频流的CPU处理压力转移到GPU。然后，在移动相机之间的刚性约束下，我们以度量尺度位姿初始化里程计系统。最后，我们在后端融合多相机的特征，以实现鲁棒的位姿估计和在线尺度优化。此外，多相机特征有助于改进用于位姿图优化的回环检测。在KITTI-360和MultiCamData数据集上的实验验证了其对任意排列相机的鲁棒性。与其他立体和多相机视觉SLAM系统相比，我们的方法获得了更高的位姿精度和更好的泛化能力。我们的代码和在线演示可在https://github.com/JunhaoWang615/MCVO获取。
英文摘要：
Making multi-camera visual SLAM systems easier to set up and more robust to the environment is attractive for vision robots. Existing monocular and binocular vision SLAM systems have narrow sensing Field-of-View (FoV), resulting in degenerated accuracy and limited robustness in textureless environments. Thus multi-camera SLAM systems are gaining attention because they can provide redundancy with much wider FoV. However, the usual arbitrary placement and orientation of multiple cameras make the pose scale estimation and system updating challenging. To address these problems, we propose a robust visual odometry system for rigidly-bundled arbitrarily-arranged multi-cameras, namely MCVO, which can achieve metric-scale state estimation with high flexibility in the cameras' arrangement. Specifically, we first design a learning-based feature tracking framework to shift the pressure of CPU processing of multiple video streams to GPU. Then we initialize the odometry system with the metric-scale poses under the rigid constraints between moving cameras. Finally, we fuse the features of the multi-cameras in the back-end to achieve robust pose estimation and online scale optimization. Additionally, multi-camera features help improve the loop detection for pose graph optimization. Experiments on KITTI-360 and MultiCamData datasets validate its robustness over arbitrarily arranged cameras. Compared with other stereo and multi-camera visual SLAM systems, our method obtains higher pose accuracy with better generalization ability. Our codes and online demos are available at https://github.com/JunhaoWang615/MCVO
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
