SLAM Using Relational Trees and Semantics

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This work attempts to develop a method for SLAM using semantics based on FastSLAM 2.0. Our approach to semantic mapping consists of segmenting images obtained from two sensors (optical and radar) aboard a UAV. We then identify landmarks within the segmented image, followed by the construction of relational trees with the landmarks; these trees are then used at consecutive time-steps of the robot’s motion for its localization as well as update of the landmarks. The term semantics has been used for region-landmarks which are validated with a look-up table (LUT) of the predefined surface type information, a superset of the robot’s actual environment. Finally, based on particle filters, the posterior density of the state of the robot is estimated and a 2-D semantics map is constructed. The methodology has been tested in a situation wherein the robot’s true environment and path have been simulated. For simulation we consider satellite images of optical and radar sensors of the robot’s environment. At different time-steps the robot’s images are cropped from these images, incorporating errors in the robot’s control information. Experiments carried out on the simulated environment have provided encouraging results.

Info:

Periodical:

Advanced Materials Research (Volumes 452-453)

Edited by:

Liu Pei

Pages:

648-653

DOI:

10.4028/www.scientific.net/AMR.452-453.648

Citation:

A. Sarkar et al., "SLAM Using Relational Trees and Semantics", Advanced Materials Research, Vols. 452-453, pp. 648-653, 2012

Online since:

January 2012

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$38.00

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