Fusing Sonars and LRF data to Perform SLAM in Reduced Visibility Scenarios
In Proc. of IEEE Int. Conf. on
Autonomous Robot Systems and Competitions (ICARSC 2014), Espinho, Portugal, pp.
116-121, May 14-15, 2014. DOI:
10.1109/ICARSC.2014.6849772
Abstract
Simultaneous Localization and Mapping (SLAM) approaches have evolved considerably in recent years. However, there are many situations which are not easily handled, such as the case of smoky, dusty, or foggy environments where commonly used range sensors for SLAM are highly disturbed by noise induced in the measurement process by particles of smoke, dust or steam. This work presents a sensor fusion method for range sensing in Simultaneous Localization and Mapping (SLAM) under reduced visibility conditions. The proposed method uses the complementary characteristics between a Laser Range Finder (LRF) and an array of sonars in order to ultimately map smoky environments. The method was validated through experiments in a smoky indoor scenario, and results showed that it is able to adequately cope with induced disturbances, thus decreasing the impact of smoke particles in the mapping task.
Index Terms — SLAM, reduced visibility, sensor fusion, LRF, sonars, fuzzy logic.
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BibTeX
@INPROCEEDINGS(Santos_et_al_14,
AUTHOR = "Santos, J. M. and
Couceiro, M. S. and Portugal, D. and Rocha, R. P.",
TITLE = "Fusing Sonars and LRF data to Perform SLAM in Reduced Visibility Scenarios",
BOOKTITLE = "Proc. of IEEE Int. Conf. on Autonomous Robot Systems and Competitions (ICARSC 2014)",
ADDRESS = "Espinho, Portugal",
YEAR = "2014",
MONTH = "May",
PAGES = "116-121"
)
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Copyright ©
2014 Rui Rocha, Dep. of Electrical and Computer Engineering,