Exploration is of interest for autonomous mapping and rescue applications using unmanned vehicles. The objective is to, without any prior information, explore all initially unmapped space. We present a system that can perform fast and efficient exploration of large scale arbitrary 3D environments. We combine frontier exploration planning (FEP) as a global planning strategy, together with receding horizon planning (RH-NBVP) for local planning. This leads to plans that incorporate information gain along the way, but do not get stuck in already explored regions. Furthermore, we make the potential information gain estimation more efficient, through sparse ray-tracing, and caching of already estimated gains. The worked carried out in this thesis has been published as a paper in Robotand Automation letters and presented at the International Conference on Robotics and Automation in Montreal 2019.
Efficient Autonomous Exploration Planning of Large-Scale 3D-Environments : A tool for autonomous 3D exploration indoor ; Effektiv Autonom Utforskningsplanering av Storskaliga 3D-Miljöer : Ett verktyg för 3D utforskning inomhus
2019-01-01
Hochschulschrift
Elektronische Ressource
Englisch
DDC: | 629 |
Efficient Autonomous Exploration Planning of Large Scale 3D-Environments
BASE | 2019
|Landskapets storskaliga struktur
British Library Conference Proceedings | 1994
|Autonomous Capabilities to Facilitate Indoor Exploration by UAS
TIBKAT | 2022
|