Towards spherical robots for mobile mapping in human made environments

Spherical robots are a format that has not been thoroughly explored for the application of mobile mapping. In contrast to other designs, it provides some unique advantages. Among those is a spherical shell that protects internal sensors and actuators from possible harsh environments, as well as an inherent rotation for locomotion that enables measurements in all directions. Mobile mapping always requires a high-precise pose knowledge to obtain consistent and correct environment maps. This is typically done by a combination of external reference sensors such as Global Navigation Satellite System (GNSS) measurements and inertial measurements or by coarsely estimating the pose using inertial measurement units (IMUs) and post processing the data by registering the different measurements to each other. In indoor environments, the GNSS reference is not an option. Hence many mobile mapping applications turn to the second option. An advantage of indoor environments is that human-made environments usually have a certain structure, such as parallel and perpendicular planes. We propose a registration procedure that exploits this structure by minimizing the distance of measured points to a corresponding plane. Further, we evaluate the procedure on a simulated dataset of an ideal corridor and on an experimentally acquired dataset with different motion profiles. We show that we nearly reproduce the ground truth for the simulated dataset and improve the average point-to-point distance to a reference scan in the experimental dataset. The presented algorithms are required to work completely autonomously.