Robust Visual Odometry Using Rigidly-Bundled Arbitrarily-Arranged Multi-Cameras

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 sensingField-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 wider FoVs. 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 the 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, MCVO obtains higher pose estimation accuracy with better robustness.