Combining terrain maps and polyhedral models for robot navigation

As an autonomous robot navigates in an unknown environment, the perception subsystem must be able to perform the incremental modeling of this environment as well as robot self-location. In this paper, the authors have chosen to combine a polyhedral representation of the world with its digital elevation model, in order to deal with autonomous navigation in semi-structured environments. The authors describe algorithms needed to build snapshot models from noisy and sparse range data, to perform 3D data fusion between these snapshot models, in order to incrementally build a reliable 3D model from which a path planner could generate safe trajectories. The authors present a fully implemented modeling strategy based on range data processing, and describe the different modeling services required in order to perform the indoor scene navigation of the robot HILARE-2