Planar features and 6D-SLAM based on linear regression Kalman filters with n-dimensional approximated Gaussians

Cihan Ulas; Hakan Temeltas

doi:10.3182/20140824-6-za-1003.02039

Planar features and 6D-SLAM based on linear regression Kalman filters with n-dimensional approximated Gaussians

Cihan Ulas, Hakan Temeltas

Department of Control and Automation Engineering

Scientific and Technological Research Council of Turkey

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

In this paper, a six-dimensional (6D) Simultaneous Localization and Mapping (SLAM) based on novel Linear Regression Kalman Filter (LRKF), called Smart Sampling Kalman Filter (S2KF), is proposed. While the conventional feature based SLAM methods use point features as landmarks, only a few take the advantage of geometric information like corners, edges, and planes. A feature based SLAM method using planar landmarks extracted from 3D Light Detection and Ranging (LiDAR) outdoor data is proposed. The method uses the LFKF with n-dimensional approximated Gaussians by addressing the data association problem based on semantic data of plane-features. Experimental results show the appropriateness of the approach, and the filter performance is compared with the traditional filters, such as Unscented Kalman Filters and Cubature Kalman Filters.

Original language	English
Title of host publication	19th IFAC World Congress IFAC 2014, Proceedings
Editors	Edward Boje, Xiaohua Xia
Publisher	IFAC Secretariat
Pages	10194-10199
Number of pages	6
ISBN (Electronic)	9783902823625
DOIs	https://doi.org/10.3182/20140824-6-za-1003.02039
Publication status	Published - 2014
Event	19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014 - Cape Town, South Africa Duration: 24 Aug 2014 → 29 Aug 2014

Publication series

Name	IFAC Proceedings Volumes (IFAC-PapersOnline)
Volume	19
ISSN (Print)	1474-6670

Conference

Conference	19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014
Country/Territory	South Africa
City	Cape Town
Period	24/08/14 → 29/08/14

Bibliographical note

Publisher Copyright:
© IFAC.

Keywords

3D feature extraction
LiDAR perception
Outdoor S2KF-SLAM
Plane detection

Access to Document

10.3182/20140824-6-za-1003.02039

Cite this

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title = "Planar features and 6D-SLAM based on linear regression Kalman filters with n-dimensional approximated Gaussians",

abstract = "In this paper, a six-dimensional (6D) Simultaneous Localization and Mapping (SLAM) based on novel Linear Regression Kalman Filter (LRKF), called Smart Sampling Kalman Filter (S2KF), is proposed. While the conventional feature based SLAM methods use point features as landmarks, only a few take the advantage of geometric information like corners, edges, and planes. A feature based SLAM method using planar landmarks extracted from 3D Light Detection and Ranging (LiDAR) outdoor data is proposed. The method uses the LFKF with n-dimensional approximated Gaussians by addressing the data association problem based on semantic data of plane-features. Experimental results show the appropriateness of the approach, and the filter performance is compared with the traditional filters, such as Unscented Kalman Filters and Cubature Kalman Filters.",

keywords = "3D feature extraction, LiDAR perception, Outdoor S2KF-SLAM, Plane detection",

author = "Cihan Ulas and Hakan Temeltas",

note = "Publisher Copyright: {\textcopyright} IFAC.; 19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014 ; Conference date: 24-08-2014 Through 29-08-2014",

year = "2014",

doi = "10.3182/20140824-6-za-1003.02039",

language = "English",

series = "IFAC Proceedings Volumes (IFAC-PapersOnline)",

publisher = "IFAC Secretariat",

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}

Ulas, C & Temeltas, H 2014, Planar features and 6D-SLAM based on linear regression Kalman filters with n-dimensional approximated Gaussians. in E Boje & X Xia (eds), 19th IFAC World Congress IFAC 2014, Proceedings. IFAC Proceedings Volumes (IFAC-PapersOnline), vol. 19, IFAC Secretariat, pp. 10194-10199, 19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014, Cape Town, South Africa, 24/08/14. https://doi.org/10.3182/20140824-6-za-1003.02039

Planar features and 6D-SLAM based on linear regression Kalman filters with n-dimensional approximated Gaussians. / Ulas, Cihan; Temeltas, Hakan.
19th IFAC World Congress IFAC 2014, Proceedings. ed. / Edward Boje; Xiaohua Xia. IFAC Secretariat, 2014. p. 10194-10199 (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 19).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Temeltas, Hakan

N1 - Publisher Copyright: © IFAC.

PY - 2014

Y1 - 2014

N2 - In this paper, a six-dimensional (6D) Simultaneous Localization and Mapping (SLAM) based on novel Linear Regression Kalman Filter (LRKF), called Smart Sampling Kalman Filter (S2KF), is proposed. While the conventional feature based SLAM methods use point features as landmarks, only a few take the advantage of geometric information like corners, edges, and planes. A feature based SLAM method using planar landmarks extracted from 3D Light Detection and Ranging (LiDAR) outdoor data is proposed. The method uses the LFKF with n-dimensional approximated Gaussians by addressing the data association problem based on semantic data of plane-features. Experimental results show the appropriateness of the approach, and the filter performance is compared with the traditional filters, such as Unscented Kalman Filters and Cubature Kalman Filters.

AB - In this paper, a six-dimensional (6D) Simultaneous Localization and Mapping (SLAM) based on novel Linear Regression Kalman Filter (LRKF), called Smart Sampling Kalman Filter (S2KF), is proposed. While the conventional feature based SLAM methods use point features as landmarks, only a few take the advantage of geometric information like corners, edges, and planes. A feature based SLAM method using planar landmarks extracted from 3D Light Detection and Ranging (LiDAR) outdoor data is proposed. The method uses the LFKF with n-dimensional approximated Gaussians by addressing the data association problem based on semantic data of plane-features. Experimental results show the appropriateness of the approach, and the filter performance is compared with the traditional filters, such as Unscented Kalman Filters and Cubature Kalman Filters.

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ER -

Planar features and 6D-SLAM based on linear regression Kalman filters with n-dimensional approximated Gaussians

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Keywords

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