Normal Distribution Mixture Matching based Model Free Object Tracking Using 2D LIDAR

Baehoon Choi; Hyung Gi Jo; Euntai Kim

doi:10.1109/IROS40897.2019.8967876

Normal Distribution Mixture Matching based Model Free Object Tracking Using 2D LIDAR

Baehoon Choi, Hyung Gi Jo, Euntai Kim

Department of Electrical and Electronic Engineering

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

1 Citation (Scopus)

Abstract

In this paper, a novel normal distribution mixture matching based model free object tracking algorithm using 2D LIDAR is proposed. Each target object is modeled as a normal distribution mixture that captures the distribution of the points scanned from the surface of the object. This novel representation enables normal distribution transform (NDT) to accurately estimate the motion of objects, even if the shape of the points differs depending on where it is observed. Our evaluation of the proposed algorithm shows good performance in practical applications. In addition, we provides an alternative way of segmentation and data association using occupancy grid map to avoid a problem that defines a distance metric between the mixture and the point cloud. As a result, the proposed algorithm works in real time in our experiments.

Original language	English
Title of host publication	2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	455-461
Number of pages	7
ISBN (Electronic)	9781728140049
DOIs	https://doi.org/10.1109/IROS40897.2019.8967876
Publication status	Published - 2019 Nov
Event	2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 - Macau, China Duration: 2019 Nov 3 → 2019 Nov 8

Publication series

Name	IEEE International Conference on Intelligent Robots and Systems
ISSN (Print)	2153-0858
ISSN (Electronic)	2153-0866

Conference

Conference	2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Country/Territory	China
City	Macau
Period	19/11/3 → 19/11/8

Bibliographical note

Funding Information:
This work was supported in part by LG Electronics, Development of multiple object tracking (MOT) algorithm in dynamic environment. This work was also supported in part by the Industry Core Technology Development Project, 20005062, Development of Artificial Intelligence Robot Autonomous Navigation Technology for Agile Movement in Crowded Space, funded by the Ministry of Trade, industry & Energy (MOTIE, Republic of Korea).

Publisher Copyright:
© 2019 IEEE.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Software
Computer Vision and Pattern Recognition
Computer Science Applications

Access to Document

10.1109/IROS40897.2019.8967876

Cite this

Choi, B., Jo, H. G., & Kim, E. (2019). Normal Distribution Mixture Matching based Model Free Object Tracking Using 2D LIDAR. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 (pp. 455-461). Article 8967876 (IEEE International Conference on Intelligent Robots and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS40897.2019.8967876

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title = "Normal Distribution Mixture Matching based Model Free Object Tracking Using 2D LIDAR",

abstract = "In this paper, a novel normal distribution mixture matching based model free object tracking algorithm using 2D LIDAR is proposed. Each target object is modeled as a normal distribution mixture that captures the distribution of the points scanned from the surface of the object. This novel representation enables normal distribution transform (NDT) to accurately estimate the motion of objects, even if the shape of the points differs depending on where it is observed. Our evaluation of the proposed algorithm shows good performance in practical applications. In addition, we provides an alternative way of segmentation and data association using occupancy grid map to avoid a problem that defines a distance metric between the mixture and the point cloud. As a result, the proposed algorithm works in real time in our experiments.",

author = "Baehoon Choi and Jo, {Hyung Gi} and Euntai Kim",

note = "Funding Information: This work was supported in part by LG Electronics, Development of multiple object tracking (MOT) algorithm in dynamic environment. This work was also supported in part by the Industry Core Technology Development Project, 20005062, Development of Artificial Intelligence Robot Autonomous Navigation Technology for Agile Movement in Crowded Space, funded by the Ministry of Trade, industry & Energy (MOTIE, Republic of Korea). Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 ; Conference date: 03-11-2019 Through 08-11-2019",

year = "2019",

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doi = "10.1109/IROS40897.2019.8967876",

language = "English",

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Choi, B, Jo, HG & Kim, E 2019, Normal Distribution Mixture Matching based Model Free Object Tracking Using 2D LIDAR. in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019., 8967876, IEEE International Conference on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 455-461, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019, Macau, China, 19/11/3. https://doi.org/10.1109/IROS40897.2019.8967876

Normal Distribution Mixture Matching based Model Free Object Tracking Using 2D LIDAR. / Choi, Baehoon; Jo, Hyung Gi; Kim, Euntai.
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 455-461 8967876 (IEEE International Conference on Intelligent Robots and Systems).

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

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N1 - Funding Information: This work was supported in part by LG Electronics, Development of multiple object tracking (MOT) algorithm in dynamic environment. This work was also supported in part by the Industry Core Technology Development Project, 20005062, Development of Artificial Intelligence Robot Autonomous Navigation Technology for Agile Movement in Crowded Space, funded by the Ministry of Trade, industry & Energy (MOTIE, Republic of Korea). Publisher Copyright: © 2019 IEEE.

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AB - In this paper, a novel normal distribution mixture matching based model free object tracking algorithm using 2D LIDAR is proposed. Each target object is modeled as a normal distribution mixture that captures the distribution of the points scanned from the surface of the object. This novel representation enables normal distribution transform (NDT) to accurately estimate the motion of objects, even if the shape of the points differs depending on where it is observed. Our evaluation of the proposed algorithm shows good performance in practical applications. In addition, we provides an alternative way of segmentation and data association using occupancy grid map to avoid a problem that defines a distance metric between the mixture and the point cloud. As a result, the proposed algorithm works in real time in our experiments.

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Choi B, Jo HG, Kim E. Normal Distribution Mixture Matching based Model Free Object Tracking Using 2D LIDAR. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 455-461. 8967876. (IEEE International Conference on Intelligent Robots and Systems). doi: 10.1109/IROS40897.2019.8967876

Normal Distribution Mixture Matching based Model Free Object Tracking Using 2D LIDAR

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