LiDAR-based classification of objects and terrain

Abstract

This paper presents the development of a LiDAR-based object classification system using machine learning and signal processing. The proposal explores Support Vector Machines (SVM) and neural networks to classify terrain with the help of a LiDAR that scans an area similarly to how a picture is taken. This project involves the processing of data to generate a point cloud that lets us visualize the scans taken by the Light Detection and Ranging (LiDAR). The dataset was built by taking multiple scans of three types of terrain, flat, grassy, and rocky. This paper shows experimental results of machine learning models built around LiDAR-acquired data and small datasets, it also shows point cloud visualizations and a simple signal processing technique.