Camera Localization in Outdoor Garden Environments Using Artificial Landmarks

Nicola Strisciuglio; Maria Leyva Vallina; Nicolai Petkov; Rafael Munoz Salinas

doi:10.1109/IWOBI.2018.8464139

Camera Localization in Outdoor Garden Environments Using Artificial Landmarks

Nicola Strisciuglio, Maria Leyva Vallina, Nicolai Petkov, Rafael Munoz Salinas

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

5 Citations (Scopus)

Abstract

In this paper, we present an outdoor monocular camera localization system based on artificial markers and test its performance in one of the test gardens of the TrimBot2020 project, in Wageningen. We use ArUco markers to construct a map of the environment and to subsequently localize the camera position within it. We combine the localization algorithm based on ArUco with a Kalman filter to smooth the trajectory and improve the localization stability with respect to fast movements of the camera, and blurred or noisy images. We recorded two sequences, with resolution 480p and l080p respectively, in the TrimBot2020 garden. We compare the localization performance of ArUco with a keypoint-based approach, namely ORB-SLAM2. We analyze and discuss the strengths and problems of both marker- and keypoint-based approaches on the considered sequences. The performed comparison suggests that the two approaches might be fused to jointly improve re-localization and reduce the drift in pose estimation.

Original language	English
Title of host publication	2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings
Place of Publication	Piscataway, NJ
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	978-1-5386-7506-9
ISBN (Print)	978-1-5386-7507-6
DOIs	https://doi.org/10.1109/IWOBI.2018.8464139
Publication status	Published - 12 Sept 2018
Externally published	Yes
Event	2018 IEEE International Work Conference on Bio-inspired Intelligence, IWOBI 2018 - San Carlos, Costa Rica Duration: 18 Jul 2018 → 20 Jul 2018

Conference

Conference	2018 IEEE International Work Conference on Bio-inspired Intelligence, IWOBI 2018
Abbreviated title	IWOBI
Country/Territory	Costa Rica
City	San Carlos
Period	18/07/18 → 20/07/18

Keywords

Cameras
Kalman filters
Trajectory
Robot vision systems
Noise measurement
Pose estimation
Feature extraction

Access to Document

10.1109/IWOBI.2018.8464139

Cite this

@inproceedings{f1587b1878154fd6a98daf7192af5c45,

title = "Camera Localization in Outdoor Garden Environments Using Artificial Landmarks",

abstract = "In this paper, we present an outdoor monocular camera localization system based on artificial markers and test its performance in one of the test gardens of the TrimBot2020 project, in Wageningen. We use ArUco markers to construct a map of the environment and to subsequently localize the camera position within it. We combine the localization algorithm based on ArUco with a Kalman filter to smooth the trajectory and improve the localization stability with respect to fast movements of the camera, and blurred or noisy images. We recorded two sequences, with resolution 480p and l080p respectively, in the TrimBot2020 garden. We compare the localization performance of ArUco with a keypoint-based approach, namely ORB-SLAM2. We analyze and discuss the strengths and problems of both marker- and keypoint-based approaches on the considered sequences. The performed comparison suggests that the two approaches might be fused to jointly improve re-localization and reduce the drift in pose estimation.",

keywords = "Cameras, Kalman filters, Trajectory, Robot vision systems, Noise measurement, Pose estimation, Feature extraction",

author = "Nicola Strisciuglio and Vallina, {Maria Leyva} and Nicolai Petkov and Salinas, {Rafael Munoz}",

year = "2018",

month = sep,

day = "12",

doi = "10.1109/IWOBI.2018.8464139",

language = "English",

isbn = "978-1-5386-7507-6",

booktitle = "2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings",

publisher = "IEEE",

address = "United States",

note = "2018 IEEE International Work Conference on Bio-inspired Intelligence, IWOBI 2018, IWOBI ; Conference date: 18-07-2018 Through 20-07-2018",

}

Strisciuglio, N, Vallina, ML, Petkov, N & Salinas, RM 2018, Camera Localization in Outdoor Garden Environments Using Artificial Landmarks. in 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings., 8464139, IEEE, Piscataway, NJ, 2018 IEEE International Work Conference on Bio-inspired Intelligence, IWOBI 2018, San Carlos, Costa Rica, 18/07/18. https://doi.org/10.1109/IWOBI.2018.8464139

Camera Localization in Outdoor Garden Environments Using Artificial Landmarks. / Strisciuglio, Nicola; Vallina, Maria Leyva; Petkov, Nicolai et al.
2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings. Piscataway, NJ: IEEE, 2018. 8464139.

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

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AU - Vallina, Maria Leyva

AU - Petkov, Nicolai

AU - Salinas, Rafael Munoz

PY - 2018/9/12

Y1 - 2018/9/12

N2 - In this paper, we present an outdoor monocular camera localization system based on artificial markers and test its performance in one of the test gardens of the TrimBot2020 project, in Wageningen. We use ArUco markers to construct a map of the environment and to subsequently localize the camera position within it. We combine the localization algorithm based on ArUco with a Kalman filter to smooth the trajectory and improve the localization stability with respect to fast movements of the camera, and blurred or noisy images. We recorded two sequences, with resolution 480p and l080p respectively, in the TrimBot2020 garden. We compare the localization performance of ArUco with a keypoint-based approach, namely ORB-SLAM2. We analyze and discuss the strengths and problems of both marker- and keypoint-based approaches on the considered sequences. The performed comparison suggests that the two approaches might be fused to jointly improve re-localization and reduce the drift in pose estimation.

AB - In this paper, we present an outdoor monocular camera localization system based on artificial markers and test its performance in one of the test gardens of the TrimBot2020 project, in Wageningen. We use ArUco markers to construct a map of the environment and to subsequently localize the camera position within it. We combine the localization algorithm based on ArUco with a Kalman filter to smooth the trajectory and improve the localization stability with respect to fast movements of the camera, and blurred or noisy images. We recorded two sequences, with resolution 480p and l080p respectively, in the TrimBot2020 garden. We compare the localization performance of ArUco with a keypoint-based approach, namely ORB-SLAM2. We analyze and discuss the strengths and problems of both marker- and keypoint-based approaches on the considered sequences. The performed comparison suggests that the two approaches might be fused to jointly improve re-localization and reduce the drift in pose estimation.

KW - Cameras

KW - Kalman filters

KW - Trajectory

KW - Robot vision systems

KW - Noise measurement

KW - Pose estimation

KW - Feature extraction

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Y2 - 18 July 2018 through 20 July 2018

ER -

Camera Localization in Outdoor Garden Environments Using Artificial Landmarks

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