Abstract
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.
Highlights
S IMULTANEOUS Localization and Mapping (SLAM) is a set of techniques to deal with correlated uncertainties in sensor movements and sensor readings in an unknown environment to simultaneously map the environment and track the sensor poses
There are several kinds of maps generated by Simultaneous Localization and Mapping (SLAM) and it depends on the sensor used
This paper describes a method of semantic SLAM using objects and walls as a model of an environment
Summary
S IMULTANEOUS Localization and Mapping (SLAM) is a set of techniques to deal with correlated uncertainties in sensor movements and sensor readings in an unknown environment to simultaneously map the environment and track the sensor poses. Sparse point map and dense reconstruction are useful models They do not contain semantic information of environments. Notable results in semantic SLAM are [3], [4], [5], [6], [7], [8] and [9] Similarities between these works are that all maps resemble point-based maps with semantic objects or walls are taking place of points. This is sufficient, having a higher degree of semantic is always desirable. This paper describes a method of semantic SLAM using objects and walls as a model of an environment.
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