@inproceedings{da8d21c769554c03bf148f0c7388a740,
title = "Adaptive computation algorithm for simultaneous localization and mapping (SLAM)",
abstract = "Computationally Efficient SLAM (CESLAM) was proposed to improve the accuracy and runtime efficiency of FastSLAM 1.0 and FastSLAM 2.0. This method adopts the landmark measurement with the maximum likelihood, where the particle state is updated before updating the landmark estimate. Also, CESLAM solves the problem of real-time performance. In this paper, a modified version of CESLAM, called adaptive computation SLAM (ACSLAM), as an adaptive SLAM enhances the localization and mapping accuracy along with better runtime performance. In an empirical evaluation in a rich environment, we show that ACSLAM runs about twice as fast as FastSLAM 2.0 and increases the accuracy of the location estimate by a factor of two.",
keywords = "CESLAM, Extended kalman filter, Fastslam, Particle filter",
author = "Kung, {Da Wei} and Hsu, {Chen Chien} and Wang, {Wei Yen} and Jacky Baltes",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2017.; 4th International Conference on Robot Intelligence Technology and Applications, RiTA 2015 ; Conference date: 14-12-2015 Through 16-12-2015",
year = "2017",
doi = "10.1007/978-3-319-31293-4_7",
language = "English",
isbn = "9783319312910",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "75--83",
editor = "Fakhri Karray and Jong-Hwan Kim and Hyun Myung and Jun Jo and Peter Sincak",
booktitle = "Robot Intelligence Technology and Applications 4 - Results from the 4th International Conference on Robot Intelligence Technology and Applications",
}