RoomSLAM: Simultaneous localization and mapping with objects and indoor layout structure
Rusli I.a, Trilaksono B.R.a, Adiprawita W.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, 40132, Indonesia
Abstract
© 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.This article presents RoomSLAM, a Simultaneous Localization and Mapping (SLAM) method for mobile robots in indoor environments where environments are modeled by points and quadrilaterals in 2D space. Points represent positions of semantic objects whereas quadrilaterals approximate the structural layout of the environment, namely rooms. The benefit of such modeling is threefold. Firstly, rooms are a logical way to partition a graph in large-scale SLAM. Secondly, rooms and objects reduce search space in data association. Lastly, the model contains a higher level of semantic, which is beneficial to autonomous robots whenever inter-room navigation is needed. The method was evaluated with two public datasets and the results were compared to those of ORBSLAM and RGBDSLAM.
Author keywords
2-D space,Data association,Indoor environment,Layout structure,Search spaces,Semantic objects,Simultaneous localization and mapping,Structural layout
Indexed keywords
Indoor mapping,Mobile robot,RGBD sensor,Semantic,SLAM
Funding details
This work was supported in part by the Ministry of Research and Technology and the Ministry of Education and Culture of Indonesia through the WCU 2020 Program managed by the Institut Teknologi Bandung. The work of Ismail Rusli was supported in part by the Telkom University.