Multi level mapping: Real time dense monocular SLAM
We present a method for Simultaneous Localization and Mapping (SLAM) using a monocular camera that is capable of reconstructing dense 3D geometry online without the aid of a graphics processing unit (GPU) Our key contribution is a multi resolution depth estimation and spatial smoothing process that exploits the correlation between low texture image regions and simple planar structure to adaptively scale the complexity of the generated keyframe depthmaps to the texture of the input imagery. High texture image regions are represented at higher resolutions to capture fine detail, while low texture regions are represented at coarser resolutions for smooth surfaces. The computational savings enabled by this approach allow for significantly increased reconstruction density and quality when compared to the state of the art. The increased depthmap density also improves tracking performance as more constraints can contribute to the pose estimation. A video of experimental results is available at http:/groups.csail.mit.edu/rrg/multi_level_mapping.
2016 IEEE International Conference on Robotics and Automation (ICRA)