In this talk, we discuss a novel approach to integrating inertial sensor data into a pose-graph free dense mapping algorithm that we call GravityFusion. A range of dense mapping algorithms have recently been proposed, though few integrate inertial sensing. We build on ElasticFusion, a particularly elegant dense mapping approach that fuses sensor information directly into small surface patches called surfels. Traditional inertial integration happens at the level of camera motion, however, a pose graph is not available here. Instead, we present a novel approach that incorporates the gravity measurements directly into the map: Each surfel is annotated by a gravity measurement, and that measurement is updated with each new observation of the surfel. We use mesh deformation, the same mechanism used for loop closure in ElasticFusion, to enforce a consistent gravity direction among all the surfels. This eliminates drift in two degrees of freedom, avoiding the typical curving of maps that are particularly pronounced in long hallways, as we qualitatively show in the experimental evaluation.
Puneet Puri is an M.S. student in the Robotics Institute at Carnegie Mellon University, advised by Prof. Michael Kaess. The primary focus of his research is towards accurate dense mapping using RGB-D and stereo camera systems. He also works on applying machine learning for damage detection on these dense models. He previously received his Bachelor degree in Computer Engineering from Bangalore University and has industry experience working on unmanned aerial systems and their deployment in the field for inspection.
Prof. Michael Kaess (Advisor)
Prof. Martial Hebert