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

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 in

SUBMITTER: Mohd Romlay MR 

PROVIDER: S-EPMC8386852 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature