RNN-based learning of compact maps for efficient robot localization

Alexander Förster, Alex Graves, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


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.
Original languageEnglish (US)
Title of host publicationESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks
Number of pages6
StatePublished - Dec 1 2007
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14


Dive into the research topics of 'RNN-based learning of compact maps for efficient robot localization'. Together they form a unique fingerprint.

Cite this