SLIM

A Scalable and Lightweight Indoor-navigation MAV as research and education platform

The SLIM hovering in front of a victim during a search and rescue competition of the camera drones lecture.

© W. A. Isop & ICG - TU Graz

Abstract

Indoor navigation with micro aerial vehicles (MAVs) is of growing importance nowadays. State of the art flight management controllers provide extensive interfaces for control and navigation, but most commonly aim for performing in outdoor navigation scenarios. Indoor navigation with MAVs is challenging, because of spatial constraints and lack of drift-free positioning systems like GPS. Instead, vision and/or inertial-based methods are used to localize the MAV against the environment. For educational purposes and moreover to test and develop such algorithms, since 2015 the so called droneSpace was established at the Institute of Computer Graphics and Vision at Graz University of Technology. It consists of a flight arena which is equipped with a highly accurate motion tracking system and further holds an extensive robotics framework for semi-autonomous MAV navigation. A core component of the droneSpace is a Scalable and Lightweight Indoor-navigation MAV design, which we call the SLIM (A detailed description of the SLIM and related projects can be found at our website: https://sites.google.com/view/w-a-isop/home/education/slim). It allows flexible vision-sensor setups and moreover provides interfaces to inject accurate pose measurements form external tracking sources to achieve stable indoor hover-flights. With this work we present capabilities of the framework and its flexibility, especially with regards to research and education at university level. We present use cases from research projects but also courses at the Graz University of Technology, whereas we discuss results and potential future work on the platform.

Keywords

SLIM, Dronespace, Micro aerial vehicle, Mobile robotics, Indoor navigation, Mapping, Research and education

Reference

Supplementary Material

In addition to our conference paper, we created a more extensive description of the SLIM.

Links

rie_2019_slim_extended.pdf