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.

On the Tunable Sparse Graph Solver for Pose Graph Optimization in Visual SLAM Problems We report a tunable sparse optimization solver that can trade a slight decrease in accuracy for significant speed improvement in pose graph optimization in visual simultaneous localization and mapping (vSLAM). The solver is designed for devices with significant computation and power constraints such as mobile phones or tablets. Two approaches have been combined in our design. The first is a graph pruning strategy by exploiting objective function structure to reduce the optimization problem size which further sparsifies the optimization problem. The second step is to accelerate each optimization iteration in solving increments for the gradientbased search in GaussNewton type optimization solver. We apply a modified Cholesky factorization and reuse the decomposition result from last iteration by using Cholesky updatedowndate to accelerate the computation. We have implemented our solver and tested it with open source data. The experimental results show that our solver can be twice as fast as the counterpart while maintaining a loss of less than 5% in accuracy.