TY - GEN
T1 - An adaptive particle filter for indoor robot localization
AU - Lang, Hao
AU - Li, Tiancheng
AU - Villarrubia, Gabriel
AU - Sun, Shudong
AU - Bajo, Javier
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - This paper develops an adaptive particle filter for indoor mobile robot localization, in which two different resampling operations are implemented to adjust the number of particles for fast and reliable computation. Since the weight updating is usually much more computationally intensive than the prediction, the first resampling-procedure so-called partial resampling is adopted before the prediction step, which duplicates the large weighted particles while reserves the rest obtaining better estimation accuracy and robustness. The second resampling, adopted before the updating step, decreases the number of particles through particle merging to save updating computation. In addition to speeding up the filter, sample degeneracy and sample impoverishment are counteracted. Simulations on a typical 1D model and for mobile robot localization are presented to demonstrate the validity of our approach.
AB - This paper develops an adaptive particle filter for indoor mobile robot localization, in which two different resampling operations are implemented to adjust the number of particles for fast and reliable computation. Since the weight updating is usually much more computationally intensive than the prediction, the first resampling-procedure so-called partial resampling is adopted before the prediction step, which duplicates the large weighted particles while reserves the rest obtaining better estimation accuracy and robustness. The second resampling, adopted before the updating step, decreases the number of particles through particle merging to save updating computation. In addition to speeding up the filter, sample degeneracy and sample impoverishment are counteracted. Simulations on a typical 1D model and for mobile robot localization are presented to demonstrate the validity of our approach.
KW - Mobile robot
KW - Monte Carlo localization
KW - Particle filter
UR - http://www.scopus.com/inward/record.url?scp=84937416469&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-19695-4_5
DO - 10.1007/978-3-319-19695-4_5
M3 - 会议稿件
AN - SCOPUS:84937416469
T3 - Advances in Intelligent Systems and Computing
SP - 45
EP - 55
BT - Ambient Intelligence - Software and Applications - 6th International Symposium on Ambient Intelligence, ISAmI 2015
A2 - Novais, Paulo
A2 - Fernández-Caballero, Antonio
A2 - González, Gabriel Villarrubia
A2 - Mohamed, Amr
A2 - Pereira, António
PB - Springer Verlag
T2 - 6th International Symposium on Ambient Intelligence, ISAmI 2015
Y2 - 3 June 2015 through 5 June 2015
ER -