scholarly journals Real-time vision-based robot localization

1993 ◽  
Vol 9 (6) ◽  
pp. 785-800 ◽  
Author(s):  
S. Atiya ◽  
G.D. Hager
Keyword(s):  
Real Time ◽  
Robot Localization

Real Time Mobile Robot Localization by Using a Laser and a Geometric Map

1995 ◽  
pp. 181-185
Author(s):  
A. de la Escalera ◽  
L. Moreno ◽  
J.M. Armingol ◽  
M.A. Salichs
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Robot Localization ◽  
Mobile Robot Localization

scholarly journals REINFORCEMENT LEARNING HELPS SLAM: LEARNING TO BUILD MAPS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 329-335
Author(s):  
N. Botteghi ◽  
B. Sirmacek ◽  
R. Schulte ◽  
M. Poel ◽  
C. Brune
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
A Priori ◽  
Simultaneous Localization And Mapping ◽  
Indoor Environments ◽  
Robot Localization ◽  
Robust Solution ◽  
Localization And Mapping ◽  
Reward Functions ◽  
Slam Algorithm

Abstract. 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.

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