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Discrimination of active dynamic objects in stereo-based visual SLAM

Tutkimustuotosvertaisarvioitu

Standard

Discrimination of active dynamic objects in stereo-based visual SLAM. / Ali, Ihtisham; Suominen, Olli; Gotchev, Atanas.

Electronic Imaging: Image Processing: Algorithms and Systems XVI. Society for Imaging Science and Technology, 2018. 463.

Tutkimustuotosvertaisarvioitu

Harvard

Ali, I, Suominen, O & Gotchev, A 2018, Discrimination of active dynamic objects in stereo-based visual SLAM. julkaisussa Electronic Imaging: Image Processing: Algorithms and Systems XVI., 463, Society for Imaging Science and Technology, 28/01/18. https://doi.org/10.2352/ISSN.2470-1173.2018.13.IPAS-463

APA

Ali, I., Suominen, O., & Gotchev, A. (2018). Discrimination of active dynamic objects in stereo-based visual SLAM. teoksessa Electronic Imaging: Image Processing: Algorithms and Systems XVI [463] Society for Imaging Science and Technology. https://doi.org/10.2352/ISSN.2470-1173.2018.13.IPAS-463

Vancouver

Ali I, Suominen O, Gotchev A. Discrimination of active dynamic objects in stereo-based visual SLAM. julkaisussa Electronic Imaging: Image Processing: Algorithms and Systems XVI. Society for Imaging Science and Technology. 2018. 463 https://doi.org/10.2352/ISSN.2470-1173.2018.13.IPAS-463

Author

Ali, Ihtisham ; Suominen, Olli ; Gotchev, Atanas. / Discrimination of active dynamic objects in stereo-based visual SLAM. Electronic Imaging: Image Processing: Algorithms and Systems XVI. Society for Imaging Science and Technology, 2018.

Bibtex - Lataa

@inproceedings{6eb1264e79374859a7915e04b07ba29e,
title = "Discrimination of active dynamic objects in stereo-based visual SLAM",
abstract = "Over the years, the problem of simultaneous localization and mapping have been substantially studied. Effective and robust techniques have been developed for mapping and localizing in an unknown environment in real-time. However, the bulk of the work presumes that the environment under observation is composed of static objects. In this study, we propose an approach aimed at localizing and mapping an environment irrespective of the motion of the objects in the scene. A hard threshold based Iterative Closest Point algorithm is used to compute transformations between point clouds that are obtained from dense stereo matching. The dynamic entities along with system noise are identified and isolated in the form of outliers of the data correspondence step. A confidence metric is defined that helps in identifying and transitioning a 3D point from static to dynamic and vice versa. The results are then verified in a 2D domain with the aid of a modified Gaussian Mixture Model based motion estimation. The dynamic objects are segmented in 3D and 2D domains for any possible analysis and decision making. The results demonstrate that the proposed approach effectively eliminates noise and isolates the dynamic objects during the mapping of the environment.",
author = "Ihtisham Ali and Olli Suominen and Atanas Gotchev",
note = "jufoid=84313",
year = "2018",
doi = "10.2352/ISSN.2470-1173.2018.13.IPAS-463",
language = "English",
publisher = "Society for Imaging Science and Technology",
booktitle = "Electronic Imaging",
address = "United States",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Discrimination of active dynamic objects in stereo-based visual SLAM

AU - Ali, Ihtisham

AU - Suominen, Olli

AU - Gotchev, Atanas

N1 - jufoid=84313

PY - 2018

Y1 - 2018

N2 - Over the years, the problem of simultaneous localization and mapping have been substantially studied. Effective and robust techniques have been developed for mapping and localizing in an unknown environment in real-time. However, the bulk of the work presumes that the environment under observation is composed of static objects. In this study, we propose an approach aimed at localizing and mapping an environment irrespective of the motion of the objects in the scene. A hard threshold based Iterative Closest Point algorithm is used to compute transformations between point clouds that are obtained from dense stereo matching. The dynamic entities along with system noise are identified and isolated in the form of outliers of the data correspondence step. A confidence metric is defined that helps in identifying and transitioning a 3D point from static to dynamic and vice versa. The results are then verified in a 2D domain with the aid of a modified Gaussian Mixture Model based motion estimation. The dynamic objects are segmented in 3D and 2D domains for any possible analysis and decision making. The results demonstrate that the proposed approach effectively eliminates noise and isolates the dynamic objects during the mapping of the environment.

AB - Over the years, the problem of simultaneous localization and mapping have been substantially studied. Effective and robust techniques have been developed for mapping and localizing in an unknown environment in real-time. However, the bulk of the work presumes that the environment under observation is composed of static objects. In this study, we propose an approach aimed at localizing and mapping an environment irrespective of the motion of the objects in the scene. A hard threshold based Iterative Closest Point algorithm is used to compute transformations between point clouds that are obtained from dense stereo matching. The dynamic entities along with system noise are identified and isolated in the form of outliers of the data correspondence step. A confidence metric is defined that helps in identifying and transitioning a 3D point from static to dynamic and vice versa. The results are then verified in a 2D domain with the aid of a modified Gaussian Mixture Model based motion estimation. The dynamic objects are segmented in 3D and 2D domains for any possible analysis and decision making. The results demonstrate that the proposed approach effectively eliminates noise and isolates the dynamic objects during the mapping of the environment.

U2 - 10.2352/ISSN.2470-1173.2018.13.IPAS-463

DO - 10.2352/ISSN.2470-1173.2018.13.IPAS-463

M3 - Conference contribution

BT - Electronic Imaging

PB - Society for Imaging Science and Technology

ER -