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|
|Number of pages||6|
|State||Published - Dec 1 2007|