Maren Bennewitz

TL;DR: An open-source framework to generate volumetric 3D environment models based on octrees and uses probabilistic occupancy estimation that represents not only occupied space, but also free and unknown areas and an octree map compression method that keeps the 3D models compact.

TL;DR: In this paper, an open-source framework is presented to generate volumetric 3D environ- ment models based on octrees and uses probabilistic occupancy estimation, which explicitly repre- sents not only occupied space, but also free and unknown areas.

TL;DR: An interactive tour-guide robot is described, which was successfully exhibited in a Smithsonian museum, and uses learning pervasively at all levels of the software architecture to address issues such as safe navigation in unmodified and dynamic environments, and short-term human-robot interaction.

Abstract: This paper describes Minerva, an interactive tour-guide robot that was successfully deployed in a Smithsonian museum. Minerva’s software is pervasively probabilistic, relying on explicit representations of uncertainty in perception and control. This article describes Minerva’s major software components, and provides a comparative analysis of the results obtained in the Smithsonian museum. During two weeks of highly successful operation, the robot interacted with thousands of people, both in the museum and through the Web, traversing more than 44km at speeds of up to 163 cm/sec in the unmodie d museum.

TL;DR: A technique for learning collections of trajectories that characterize typical motion patterns of persons and how to incorporate the probabilistic belief about the potential trajectories of persons into the path planning process of a mobile robot is proposed.