13 April 2018 Three main paradigms of simultaneous localization and mapping (SLAM) problem
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Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106961P (2018) https://doi.org/10.1117/12.2310094
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria
Simultaneous Localization and Mapping (SLAM) is one of the most challenging research areas within computer and machine vision for automated scene commentary and explanation. The SLAM technique has been a developing research area in the robotics context during recent years. By utilizing the SLAM method robot can estimate the different positions of the robot at the distinct points of time which can indicate the trajectory of robot as well as generate a map of the environment. SLAM has unique traits which are estimating the location of robot and building a map in the various types of environment. SLAM is effective in different types of environment such as indoor, outdoor district, Air, Underwater, Underground and Space. Several approaches have been investigated to use SLAM technique in distinct environments. The purpose of this paper is to provide an accurate perceptive review of case history of SLAM relied on laser/ultrasonic sensors and camera as perception input data. In addition, we mainly focus on three paradigms of SLAM problem with all its pros and cons. In the future, use intelligent methods and some new idea will be used on visual SLAM to estimate the motion intelligent underwater robot and building a feature map of marine environment.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Vandad Imani, Vandad Imani, Keijo Haataja, Keijo Haataja, Pekka Toivanen, Pekka Toivanen, "Three main paradigms of simultaneous localization and mapping (SLAM) problem", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961P (13 April 2018); doi: 10.1117/12.2310094; https://doi.org/10.1117/12.2310094


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