REINFORCEMENT LEARNING HELPS SLAM: LEARNING TO BUILD MAPS

Botteghi, N.; Sirmacek, B.; Schulte, R.; Poel, M.; Brune, C.

In this research, we investigate the use of Reinforcement Learning (RL) for an effective and robust solution for exploring unknown and indoor environments and reconstructing their maps. We benefit from a Simultaneous Localization and Mapping (SLAM) algorithm for real-time robot localization and mapping. Three different reward functions are compared and tested in different environments with growing complexity. The performances of the three different RL-based path planners are assessed not only on the training environments, but also on an a priori unseen environment to test the generalization properties of the policies. The results indicate that RL-based planners trained to maximize the coverage of the map are able to consistently explore and construct the maps of different indoor environments.

Zitieren

Zitierform:

Botteghi, N. / Sirmacek, B. / Schulte, R. / et al: REINFORCEMENT LEARNING HELPS SLAM: LEARNING TO BUILD MAPS. 2020. Copernicus Publications.

Zugriffsstatistik

Gesamt:
Volltextzugriffe:
Metadatenansicht:
12 Monate:
Volltextzugriffe:
Metadatenansicht:

Grafik öffnen

Rechte

Rechteinhaber: N. Botteghi et al.

Nutzung und Vervielfältigung:

Export