Simultaneous localization and mapping with infinite planes

Simultaneous localization and mapping with infinite planes is attractive because of the reduced complexity with respect to both sparse point-based and dense volumetric methods. We show how to include infinite planes into a least-squares formulation for mapping, using a homogeneous plane parametrization with a corresponding minimal representation for the optimization. Because it is a minimal representation, it is suitable for use with Gauss-Newton, Powell's Dog Leg and incremental solvers such as iSAM. We also introduce a relative plane formulation that improves convergence. We evaluate our proposed approach on simulated data to show its advantages over alternative solutions. We also introduce a simple mapping system and present experimental results, showing real-time mapping of select indoor environments with a hand-held RGB-D sensor.