Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud.

Muhammad Rabani Mohd Romlay,Ibrahim Venkat,Siti Fauziah Toha,Philippe De Wilde,Azhar Mohd Ibrahim,Jie Zhang

doi:10.1371/journal.pone.0256665

Muhammad Rabani Mohd Romlay, Ibrahim Venkat + Show 4 more

Open Access

https://doi.org/10.1371/journal.pone.0256665

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Abstract

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.

Highlights

A LiDAR sensor provides a solution for mobile applications where the system needs to be compact, lightweight and handy [1, 2]
We prove that object recognition can be obtained using a single unit, single stripe, actuated LiDAR with low computing necessity via the fusion of Clustered Extraction and Centroid Based Clustered Extraction (CE-CBCE) methods to accomplish high-reliability object recognition from sparse LiDAR point cloud data
Post CE and CBCE extraction, the collective features are optimized with kNN, decision tree (DT) and convolutional neural network (CNN)

Summary

Introduction

A LiDAR sensor provides a solution for mobile applications where the system needs to be compact, lightweight and handy [1, 2]. It has a 360 ̊ degree field of view [3], possesses high accuracy of distance measurement and in contrast to a camera, it does not depend on the light intensity of the surroundings [4, 5]. In some applications where portability and mobility are of prime importance, a single sensor which acts as the detection system is required [10, 11]

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