SLAM for a small UAV with compensation for unordinary observations and convergence analysis

Takumi Shinohara, Toru Namerikawa

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

1 Citation (Scopus)

Abstract

This paper deals with the Simultaneous Localization and Mapping (SLAM) problem for a small Unmanned Aerial Vehicle (UAV) via extended Kalman Filter (EKF) with compensations for unordinary observations. In the SLAM problem, a robot sometimes loses its proper observations then the estimation accuracy deteriorates. In this paper, to remove the effects of unordinary observations, we propose a novel SLAM method considering unordinary observation based on Mahalanobis distance. The proposed method detects the unordinary observations by comparing the observation values with its estimation and determines the weight of these observations. The convergence of the state error covariance matrix is proven. In experimental validation, we show that the UAV state and environment information can be estimated with the proposed method.

Original languageEnglish
Title of host publication2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1252-1257
Number of pages6
ISBN (Electronic)9784907764500
DOIs
Publication statusPublished - 2016 Nov 18
Event55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016 - Tsukuba, Japan
Duration: 2016 Sep 202016 Sep 23

Other

Other55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016
CountryJapan
CityTsukuba
Period16/9/2016/9/23

Fingerprint

pilotless aircraft
Simultaneous Localization and Mapping
Unmanned aerial vehicles (UAV)
Convergence Analysis
Extended Kalman filters
Covariance matrix
Robots
Mahalanobis Distance
Experimental Validation
Kalman filters
Observation
Compensation and Redress
robots
Kalman Filter
Robot

Keywords

  • Convergence Analysis
  • Extended Kalman Filter (EKF)
  • UAV SLAM

ASJC Scopus subject areas

  • Control and Optimization
  • Instrumentation
  • Control and Systems Engineering

Cite this

Shinohara, T., & Namerikawa, T. (2016). SLAM for a small UAV with compensation for unordinary observations and convergence analysis. In 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016 (pp. 1252-1257). [7749190] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SICE.2016.7749190

SLAM for a small UAV with compensation for unordinary observations and convergence analysis. / Shinohara, Takumi; Namerikawa, Toru.

2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1252-1257 7749190.

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

Shinohara, T & Namerikawa, T 2016, SLAM for a small UAV with compensation for unordinary observations and convergence analysis. in 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016., 7749190, Institute of Electrical and Electronics Engineers Inc., pp. 1252-1257, 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016, Tsukuba, Japan, 16/9/20. https://doi.org/10.1109/SICE.2016.7749190
Shinohara T, Namerikawa T. SLAM for a small UAV with compensation for unordinary observations and convergence analysis. In 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1252-1257. 7749190 https://doi.org/10.1109/SICE.2016.7749190
Shinohara, Takumi ; Namerikawa, Toru. / SLAM for a small UAV with compensation for unordinary observations and convergence analysis. 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1252-1257
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