Open AccessNovel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud
Author(s) -
Muhammad Rabani Mohd Romlay,
Azhar Mohd Ibrahim,
Siti Fauziah Toha,
Philippe De Wilde,
Ibrahim Venkat
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0256665
Subject(s) - lidar , computer science , point cloud , feature extraction , artificial intelligence , pattern recognition (psychology) , classifier (uml) , convolutional neural network , leverage (statistics) , object detection , centroid , contextual image classification , computer vision , remote sensing , image (mathematics) , geography
Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed.