Line and Plane based Incremental Surface Reconstruction

Abstract

Simultaneous Localization and Mapping(SLAM) has been very popular in the past and is gaining more traction in the era of autonomous vehicle research and robot manipulation. Computing accurate surface models from sparse Visual SLAM 3D point clouds is difcult. There have been works where this problem was addressed by space carving methods using map points and lines generated by those points. These methods come with their own drawbacks as point clouds and lines alone don’t add sufcient structural information to the scene. In this thesis, we try to take the natural step to also compute and verify 3D planes bottom-up from lines. Our system takes the real-time stream of new cameras and 3D points from a SLAM system and incrementally builds the 3D scene surface model. In previous work, 3D line segments were detected in relevant keyframes and were fed to the modeling algorithm for surface reconstruction. This method has an immediate drawback as some of the line segments generated in every keyframe are redundant and mark similar objects(shifted) creating clutter in the map. To avoid this issue, we track the 3D planes detected over keyframes for consistency and data association. Furthermore, the smoother and better-aligned model surfaces result in more photo-realistic rendering using keyframe texture images. Compared to other incremental real-time surface reconstruction methods, our model has less than half the triangles, and we achieve better metric reconstruction accuracy on the EuRoC MAV Benchmarks. We also tested our method on various of-the-shelf cameras for better generalization