Abstract
In this paper, we design and develop two SLAM (simultaneous localization and mapping) methods: FTF-VO (frame-to-frame visual odometry) and MF-VO (multi-frame visual odometry) for robotic localization based on stereo cameras. In both VO methods, we proposed an ARFM-based 3D reconstruction method and calculated the position of the camera using PNP, after that BA (bundle adjustment) can be used to optimize the final camera position. We tested FTF-VO and MF-VO using KITTI dataset, with BA and without BA respectively. The result demonstrates that MF-VO with BA has the highest precision so that it is suitable for the scenarios requiring high precision. Compared with FTF-VO with BA, MF-VO achieves better localization precision while requires less running time, so FTF-VO with BA is recommended for medium localization precision. FTF-VO is the best choice for low precision.
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