3D PANORAMA RECONSTRUCTION BASED ON SUBMAP JOINING

We present a new approach for constructing the 3D panorama for an indoor environment by joining together aligned submaps. For each submap, the trajectory of the moving camera is estimated based on the Kanade-LucasTomasi (KLT) features. Our method can update the feature set status by adding new features and removing expiring ones adaptively to accommodate scene changes. The accuracy of the estimated poses is further improved through sparse bundle adjustment. Furthermore, we utilize a linear optimization framework to align all submaps to obtain a consistently extended 3D panorama and to refine the visual odometry at the same time. We evaluated our approach on publicly available benchmark datasets. The experiments demonstrate that the proposed method achieves low translational drift and is robust even when the camera moves very fast.