RNN-based learning of compact maps for efficient robot localization

Alexander Förster; Alex Graves; Jürgen Schmidhuber

RNN-based learning of compact maps for efficient robot localization

Alexander Förster, Alex Graves, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	ESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks
Pages	537-542
Number of pages	6
State	Published - Dec 1 2007
Externally published	Yes

Bibliographical note

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

Cite this

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title = "RNN-based learning of compact maps for efficient robot localization",

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.",

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year = "2007",

month = dec,

day = "1",

language = "English (US)",

isbn = "2930307099",

pages = "537--542",

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AU - Förster, Alexander

AU - Graves, Alex

AU - Schmidhuber, Jürgen

N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14

PY - 2007/12/1

Y1 - 2007/12/1

N2 - 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.

AB - 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.

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M3 - Conference contribution

SN - 2930307099

SP - 537

EP - 542

BT - ESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks

ER -

RNN-based learning of compact maps for efficient robot localization

Abstract

Bibliographical note

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