Alexander Eryomin, Ramil Safin, Tatyana Tsoy, Roman Lavrenov, Evgeni Magid
Department of Intelligent Robotics, Kazan Federal University, Kazan, Russia
pp. 127–130
ABSTRACT
Map construction, or mapping, plays an important role in robotic applications. Mapping relies on inherently noisy sensor measurements to construct an accurate representation of a surrounding environment. Generally, individual sensors suffer from performance degradation issues under certain conditions in the environment. Sensor fusion enables to obtain statistically more accurate perception and to cope with performance degradation issues by combining data from multiple sensors of different modalities. This paper describes the latest developments in data fusion and state-of-the-art mapping methods using data fusion.

ARTICLE INFO
Article History
Received 21 November 2022
Accepted 20 October 2023

Keywords
Data fusion
Mapping
SLAM
Machine learning

JAALR10203

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