TY - GEN
T1 - Object recognition and classification of 2D-SLAM using machine learning and deep learning techniques
AU - Lin, Yu Fu
AU - Yang, Lee Jang
AU - Yu, Chun Yen
AU - Peng, Chao Chung
AU - Huang, Der Chen
N1 - Funding Information:
ACKNOWLEDGEMENT This work was supported by the Ministry of Science and Technology under Grant No. MOST 107-2221-E-006-114-MY3, MOST 108-2923-E-006-005-MY3 and MOST 108-2813-C-006-051-E.
Publisher Copyright:
© 2020 IEEE
PY - 2020/11
Y1 - 2020/11
N2 - Reviewing two-dimensional simultaneous localization and mapping (2D-SLAM) studies in these decades, many researchers focused on the algorithm enhancement for real-time localization and mapping. The related techniques of 2D-SLAM have been investigated deeply. However, most of the researches focus on the SLAM. Less concentration is put on 2D grid map object recognitions and labeling. Therefore, this paper dedicates to integrate recent popular machining learning techniques with 2D-SLAM technology to come out with an application for 2D object segmentation, feature extraction, as well as pattern recognition. Based on a given 2D grid map and a couple of pre-trained patterns, a clustering method and a machining learning based pattern recognition were presented. Experiments show that the proposed process is able to provide satisfactory object identification accuracy.
AB - Reviewing two-dimensional simultaneous localization and mapping (2D-SLAM) studies in these decades, many researchers focused on the algorithm enhancement for real-time localization and mapping. The related techniques of 2D-SLAM have been investigated deeply. However, most of the researches focus on the SLAM. Less concentration is put on 2D grid map object recognitions and labeling. Therefore, this paper dedicates to integrate recent popular machining learning techniques with 2D-SLAM technology to come out with an application for 2D object segmentation, feature extraction, as well as pattern recognition. Based on a given 2D grid map and a couple of pre-trained patterns, a clustering method and a machining learning based pattern recognition were presented. Experiments show that the proposed process is able to provide satisfactory object identification accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85104828840&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85104828840&partnerID=8YFLogxK
U2 - 10.1109/IS3C50286.2020.00129
DO - 10.1109/IS3C50286.2020.00129
M3 - Conference contribution
AN - SCOPUS:85104828840
T3 - Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020
SP - 473
EP - 476
BT - Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020
Y2 - 13 November 2020 through 16 November 2020
ER -