Assembling massive datasets from a large number of individual point clouds is an important part of mobile robotics research. This allows robots to see beyond their immediate surroundings, localize in both 2D and 3D, and share large-scale maps built by other robots. One of the challenges here is how to efficiently estimate and correct the pose error in the trajectory of the robot, without sacrificing accuracy. For example, correcting high-dimensional registration data graphs that represent a large building or a city can take a very long time.
During his internship, Jochen ported his registration framework called ELCH (Explicit Loop Closing Heuristic) into the Point Cloud Library framework. ELCH tries to correct collected sensor data by finding loops in the robot trajectory, estimating the pose error the robot accumulated while driving along the loop, using point cloud registration, and distributing the error over the complete robot pathway. Our hope is that using techniques like ELCH, we will be able to scale the type of environments mobile robots can operate in.