Indoor Mapping Using Machine Learning Based Classification of 3D Point Clouds
18th International Conference on Location Based Services (LBS 2023), Ghent, Belgium, 20 - 22 November 2023, pp.77-81, (Full Text)
- Publication Type: Conference Paper / Full Text
- City: Ghent
- Country: Belgium
- Page Numbers: pp.77-81
- Yıldız Technical University Affiliated: Yes
Abstract
Today, indoor maps remain a valuable source of
spatial information for various indoor environments. Classifying 3D point
clouds from indoor environments is crucial for indoor mapping. In this study,
indoor point clouds from the S3DIS dataset were classified using Random Forest
(RF), eXtreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Attentive
Interpretable Tabular Learning (TabNet). The classification performances, based
on overall accuracy and F1 scores, can be ranked as RF, MLP, XGBoost, and
TabNet. It has been determined that machine learning algorithms can be used to
classify indoor point clouds for indoor mapping.