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RNN-based learning of compact maps for efficient robot localization

By Alexander Förster, Alex Graves and Jürgen Schmidhuber

Abstract

We describe a new algorithm for robot localization, efficient both in terms of memory and processing time. It transforms a stream of laser range sensor data into a probabilistic calculation of the robot’s position, using a bidirectional Long Short-Term Memory (LSTM) recurrent neural network (RNN) to learn the structure of the environment and to answer queries such as: in which room is the robot? To achieve this, the RNN builds an implicit map of the environment

Year: 2007
OAI identifier: oai:CiteSeerX.psu:10.1.1.218.3195
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