Visual Odometry Based 3D-Reconstruction

3D reconstruction is a key technology for robots to explore unknown environments. The 3D model of the scene can be used to navigate the robot or used for scene segmentation. But when the scenes become larger or the features turns to rare, the frequently-used multi-scales matching and optimization algorithm of images and point cloud cannot work well. In this paper, we make the first attempt to split the 3D-reconstruction structure into 2 parts: pose estimation with the help of RGB images and reconstruction using poses from the aforementioned part and depth images from RGB-D cameras. All of these efforts are for accelerating the speed of pose estimation and promoting the precision of the reconstructed model, the results shows the proposed algorithm in the paper can do better than other reconstruction algorithms.