Fast segmentation of 3D point clouds

A paradigm on LiDAR data for autonomous vehicle applications

Dimitris Zermas, Izzat Izzat, Nikolaos P Papanikolopoulos

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

13 Citations (Scopus)

Abstract

The recent activity in the area of autonomous vehicle navigation has initiated a series of reactions that stirred the automobile industry, pushing for the fast commercialization of this technology which, until recently, seemed futuristic. The LiDAR sensor is able to provide a detailed understanding of the environment surrounding the vehicle making it useful in a plethora of autonomous driving scenarios. Segmenting the 3D point cloud that is provided by modern LiDAR sensors, is the first important step towards the situational assessment pipeline that aims for the safety of the passengers. This step needs to provide accurate segmentation of the ground surface and the obstacles in the vehicle's path, and to process each point cloud in real time. The proposed pipeline aims to solve the problem of 3D point cloud segmentation for data received from a LiDAR in a fast and low complexity manner that targets real world applications. The two-step algorithm first extracts the ground surface in an iterative fashion using deterministically assigned seed points, and then clusters the remaining non-ground points taking advantage of the structure of the LiDAR point cloud. Our proposed algorithms outperform similar approaches in running time, while producing similar results and support the validity of this pipeline as a segmentation tool for real world applications.

Original languageEnglish (US)
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5067-5073
Number of pages7
ISBN (Electronic)9781509046331
DOIs
StatePublished - Jul 21 2017
Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
Duration: May 29 2017Jun 3 2017

Other

Other2017 IEEE International Conference on Robotics and Automation, ICRA 2017
CountrySingapore
CitySingapore
Period5/29/176/3/17

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Zermas, D., Izzat, I., & Papanikolopoulos, N. P. (2017). Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications. In ICRA 2017 - IEEE International Conference on Robotics and Automation (pp. 5067-5073). [7989591] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2017.7989591

Fast segmentation of 3D point clouds : A paradigm on LiDAR data for autonomous vehicle applications. / Zermas, Dimitris; Izzat, Izzat; Papanikolopoulos, Nikolaos P.

ICRA 2017 - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., 2017. p. 5067-5073 7989591.

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

Zermas, D, Izzat, I & Papanikolopoulos, NP 2017, Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications. in ICRA 2017 - IEEE International Conference on Robotics and Automation., 7989591, Institute of Electrical and Electronics Engineers Inc., pp. 5067-5073, 2017 IEEE International Conference on Robotics and Automation, ICRA 2017, Singapore, Singapore, 5/29/17. https://doi.org/10.1109/ICRA.2017.7989591
Zermas D, Izzat I, Papanikolopoulos NP. Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications. In ICRA 2017 - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc. 2017. p. 5067-5073. 7989591 https://doi.org/10.1109/ICRA.2017.7989591
Zermas, Dimitris ; Izzat, Izzat ; Papanikolopoulos, Nikolaos P. / Fast segmentation of 3D point clouds : A paradigm on LiDAR data for autonomous vehicle applications. ICRA 2017 - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 5067-5073
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