Accurate Visual-Inertial SLAM by Manhattan Frame Re-identification
Xiongfeng Peng,Zhihua Liu,Qiang Wang,Yun-Tae Kim,Hong-Seok Lee,Xiongfeng Peng,Zhihua Liu,Qiang Wang,Yun-Tae Kim,Hong-Seok Lee
Most of the state-of-the-art visual-inertial SLAM methods pay less attention to the scene structure of man-made environments. In this paper, based on the assumption of multiple local Manhattan worlds (MWs), we propose a Manhattan frame (MF) re-identification method to build relative rotation constraints between MF matching pairs and tightly couple these constraints into global bundle adjust module...
