Machining Learning for 2D-SLAM Object Classification and Recognition

Chun Yen Yu, Chao Chung Peng

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

Abstract

Artificial intelligence has being widely used in the last decade, and the relevant machine learning (ML) topics are also attracted more and more attention. Therefore, in this note, certain ML related algorithms are integrated into two dimensional simultaneous localization and mapping (2D-SLAM) technology in order to give meaningful labels to certain specific objects in the environment. For the 2D-SLAM technology, the main objective is to reconstruct a binary map of the unknown environment. However, 2D-SLAM itself does not have the capability of recognizing scanning objects. To extend the application of 2D-SLAM, in this study, point cloud clustering in conjunction with image preprocessing are used in the ML methods. The proposed method can predict the label of the clustered point clouds. Consequently, the 2D-SLAM could achieve environment awareness applications, for example, forest tree counting use. Based on a given bunch of training data, this research show that the model training accuracy is 99.89%, the validation accuracy is 97.96%, and the testing accuracy could achieve 95.96%; Finally, the predicted accuracy for the given SLAM map can be up to 80%.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173993
DOIs
Publication statusPublished - 2020 Sep 28
Event7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, Taiwan
Duration: 2020 Sep 282020 Sep 30

Publication series

Name2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Conference

Conference7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
CountryTaiwan
CityTaoyuan
Period20-09-2820-09-30

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing
  • Electrical and Electronic Engineering
  • Instrumentation

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