Online Manhattan Keyframebased Dense Reconstruction from Indoor Depth Sequences We present an online framework for dense 3D reconstruction of indoor scenes using sequential Manhattan keyframes. We take advantage of the global geometry of indoor scenes extracted by Manhattan frames and pose optimization to enhance the accuracy and robustness of the reconstructed models. During sequential reconstruction, a Manhattan frame is extracted for each keyframe after surface normal adjustment, and used for a Manhattan keyframebased planar alignment to initialize the surface registration while a pose graph optimization is used to refine camera poses. The final model is created by integrating the Manhattan keyframes into the unified volumetric model using refined pose estimations. Experimental results demonstrate the advantage of our geometrybased approach to reduce the cumulative registration error and overall geometric drift.

Optimization for RGBD SLAM Based on Plane Geometrical Constraint We present an indoor RGBD SLAM optimization algorithm capable of reducing poses drift based on plane geometrical constraints and reconstructing plane structural model incrementally. Our approach extracts planes from keyframes in backend, merges oversegmented planes, establishes observation constraints between keyframes and global landmark planes, and optimizes poses of keyframes and global landmark planes in a general framework for graph optimization (g2o). Moreover, in order to prevent structural constraints between global landmark planes from being destroyed in optimization process, plane angle structural constraints between global landmark planes observed by the same keyframe are added into optimization graph. We test our optimization algorithm on standard RGBD benchmarks containing rich plane features, demonstrating that our approach can reduce poses drift and the reconstructed plane structural model covers the most part of planar regions of environment. Furthermore, the application feasibility of augmented reality (AR) is tested using reconstructed plane structural model, demonstrating that plane structural model reconstructed by our approach is suitable for AR application.