Enhanced simultaneous localization and map building

Tung Yuan Lin, Chen-Chien James Hsu, Wei Yen Wang, Yin Tien Wang

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

1 Citation (Scopus)

Abstract

FastSLAM is a well-known algorithm with its purpose to process the simultaneous localization and mapping (SLAM). There are two main FastSLAM algorithms, i.e., FastSLAM 1.0 and FastSLAM 2.0. However, the speed of execution is too slow due to the superabundant comparisons of every single existing landmarks. Thus computationally efficient SLAM (CESLAM) was presented to deal with the problem and to achieve the goal of real-time processing design. Nevertheless, there is a great possibility that large errors may occur, because the original CESLAM only takes odometer information to estimate the robot's pose in particles. Therefore, this paper not only utilizes the odometer information but also the measurement information from sensors. Finally, simulation results are illustrated that the modified version of CESLAM algorithm can effectively ameliorate the accuracy of localization and mapping.

Original languageEnglish
Title of host publicationCACS 2014 - 2014 International Automatic Control Conference, Conference Digest
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages122-127
Number of pages6
ISBN (Electronic)9781479945849
DOIs
Publication statusPublished - 2014 Apr 28
Event2014 International Automatic Control Conference, CACS 2014 - Kaohsiung, Taiwan
Duration: 2014 Nov 262014 Nov 28

Publication series

NameCACS 2014 - 2014 International Automatic Control Conference, Conference Digest

Other

Other2014 International Automatic Control Conference, CACS 2014
CountryTaiwan
CityKaohsiung
Period14/11/2614/11/28

Fingerprint

Robots
Sensors
Processing

Keywords

  • CESLAM
  • FastSLAM
  • extended Kalman filter
  • particle filter

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Lin, T. Y., Hsu, C-C. J., Wang, W. Y., & Wang, Y. T. (2014). Enhanced simultaneous localization and map building. In CACS 2014 - 2014 International Automatic Control Conference, Conference Digest (pp. 122-127). [7097174] (CACS 2014 - 2014 International Automatic Control Conference, Conference Digest). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CACS.2014.7097174

Enhanced simultaneous localization and map building. / Lin, Tung Yuan; Hsu, Chen-Chien James; Wang, Wei Yen; Wang, Yin Tien.

CACS 2014 - 2014 International Automatic Control Conference, Conference Digest. Institute of Electrical and Electronics Engineers Inc., 2014. p. 122-127 7097174 (CACS 2014 - 2014 International Automatic Control Conference, Conference Digest).

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

Lin, TY, Hsu, C-CJ, Wang, WY & Wang, YT 2014, Enhanced simultaneous localization and map building. in CACS 2014 - 2014 International Automatic Control Conference, Conference Digest., 7097174, CACS 2014 - 2014 International Automatic Control Conference, Conference Digest, Institute of Electrical and Electronics Engineers Inc., pp. 122-127, 2014 International Automatic Control Conference, CACS 2014, Kaohsiung, Taiwan, 14/11/26. https://doi.org/10.1109/CACS.2014.7097174
Lin TY, Hsu C-CJ, Wang WY, Wang YT. Enhanced simultaneous localization and map building. In CACS 2014 - 2014 International Automatic Control Conference, Conference Digest. Institute of Electrical and Electronics Engineers Inc. 2014. p. 122-127. 7097174. (CACS 2014 - 2014 International Automatic Control Conference, Conference Digest). https://doi.org/10.1109/CACS.2014.7097174
Lin, Tung Yuan ; Hsu, Chen-Chien James ; Wang, Wei Yen ; Wang, Yin Tien. / Enhanced simultaneous localization and map building. CACS 2014 - 2014 International Automatic Control Conference, Conference Digest. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 122-127 (CACS 2014 - 2014 International Automatic Control Conference, Conference Digest).
@inproceedings{1330869900ad4e5db33e3793420c0e41,
title = "Enhanced simultaneous localization and map building",
abstract = "FastSLAM is a well-known algorithm with its purpose to process the simultaneous localization and mapping (SLAM). There are two main FastSLAM algorithms, i.e., FastSLAM 1.0 and FastSLAM 2.0. However, the speed of execution is too slow due to the superabundant comparisons of every single existing landmarks. Thus computationally efficient SLAM (CESLAM) was presented to deal with the problem and to achieve the goal of real-time processing design. Nevertheless, there is a great possibility that large errors may occur, because the original CESLAM only takes odometer information to estimate the robot's pose in particles. Therefore, this paper not only utilizes the odometer information but also the measurement information from sensors. Finally, simulation results are illustrated that the modified version of CESLAM algorithm can effectively ameliorate the accuracy of localization and mapping.",
keywords = "CESLAM, FastSLAM, extended Kalman filter, particle filter",
author = "Lin, {Tung Yuan} and Hsu, {Chen-Chien James} and Wang, {Wei Yen} and Wang, {Yin Tien}",
year = "2014",
month = "4",
day = "28",
doi = "10.1109/CACS.2014.7097174",
language = "English",
series = "CACS 2014 - 2014 International Automatic Control Conference, Conference Digest",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "122--127",
booktitle = "CACS 2014 - 2014 International Automatic Control Conference, Conference Digest",

}

TY - GEN

T1 - Enhanced simultaneous localization and map building

AU - Lin, Tung Yuan

AU - Hsu, Chen-Chien James

AU - Wang, Wei Yen

AU - Wang, Yin Tien

PY - 2014/4/28

Y1 - 2014/4/28

N2 - FastSLAM is a well-known algorithm with its purpose to process the simultaneous localization and mapping (SLAM). There are two main FastSLAM algorithms, i.e., FastSLAM 1.0 and FastSLAM 2.0. However, the speed of execution is too slow due to the superabundant comparisons of every single existing landmarks. Thus computationally efficient SLAM (CESLAM) was presented to deal with the problem and to achieve the goal of real-time processing design. Nevertheless, there is a great possibility that large errors may occur, because the original CESLAM only takes odometer information to estimate the robot's pose in particles. Therefore, this paper not only utilizes the odometer information but also the measurement information from sensors. Finally, simulation results are illustrated that the modified version of CESLAM algorithm can effectively ameliorate the accuracy of localization and mapping.

AB - FastSLAM is a well-known algorithm with its purpose to process the simultaneous localization and mapping (SLAM). There are two main FastSLAM algorithms, i.e., FastSLAM 1.0 and FastSLAM 2.0. However, the speed of execution is too slow due to the superabundant comparisons of every single existing landmarks. Thus computationally efficient SLAM (CESLAM) was presented to deal with the problem and to achieve the goal of real-time processing design. Nevertheless, there is a great possibility that large errors may occur, because the original CESLAM only takes odometer information to estimate the robot's pose in particles. Therefore, this paper not only utilizes the odometer information but also the measurement information from sensors. Finally, simulation results are illustrated that the modified version of CESLAM algorithm can effectively ameliorate the accuracy of localization and mapping.

KW - CESLAM

KW - FastSLAM

KW - extended Kalman filter

KW - particle filter

UR - http://www.scopus.com/inward/record.url?scp=84949928812&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84949928812&partnerID=8YFLogxK

U2 - 10.1109/CACS.2014.7097174

DO - 10.1109/CACS.2014.7097174

M3 - Conference contribution

T3 - CACS 2014 - 2014 International Automatic Control Conference, Conference Digest

SP - 122

EP - 127

BT - CACS 2014 - 2014 International Automatic Control Conference, Conference Digest

PB - Institute of Electrical and Electronics Engineers Inc.

ER -