A Novel Metric Online Monocular SLAM Approach for Indoor Applications

Abstract

Monocular SLAM has attracted more attention recently due to its flexibility and being economic. In this paper, a novel metric online direct monocular SLAM approach is proposed, which can obtain the metric reconstruction of the scene. In the proposed approach, a chessboard is utilized to provide initial depth map and scale correction information during the SLAM process. The involved chessboard provides the absolute scale of scene, and it is seen as a bridge between the camera visual coordinate and the world coordinate. The scene is reconstructed as a series of key frames with their poses and correlative semidense depth maps, using a highly accurate pose estimation achieved by direct grid point-based alignment. The estimated pose is coupled with depth map estimation calculated by filtering over a large number of pixelwise small-baseline stereo comparisons. In addition, this paper formulates the scale-drift model among key frames and the calibration chessboard is used to correct the accumulated pose error. At the end of this paper, several indoor experiments are conducted. The results suggest that the proposed approach is able to achieve higher reconstruction accuracy when compared with the traditional LSD-SLAM approach. And the approach can also run in real time on a commonly used computer.