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RFS-SLAM robot: An experimental platform for RFS based occupancy-grid SLAM | IEEE Conference Publication | IEEE Xplore

RFS-SLAM robot: An experimental platform for RFS based occupancy-grid SLAM

Publisher: IEEE

Abstract:

This paper describes the implementation of a miniature open-source and cost-effective SLAM-robot, utilizing a novel occupancy-grid SLAM algorithm based on the concept of ...View more

Abstract:

This paper describes the implementation of a miniature open-source and cost-effective SLAM-robot, utilizing a novel occupancy-grid SLAM algorithm based on the concept of random-finite-sets (RFS). This robotic platform is remotely controlled to move and scan unknown environments using a differential drive system algorithm, sending instantaneous position feedback to the remote operator. The mobile robot utilizes a LIDAR-Lite 2 laser range finder to map the environment while simultaneously estimating its position and orientation within the map. Even though there are many mobile robots that implement this behavior, the main advantage in this proposed robotic platform is modeling of LIDAR measurements at each scan as a RFS. This model provides robustness against the random count of received returns, due to false and missed detections, allowing the use of an inexpensive LIDAR sensor and commercial off the shelf hardware.
Date of Conference: 10-13 July 2017
Date Added to IEEE Xplore: 14 August 2017
ISBN Information:
Publisher: IEEE
Conference Location: Xi'an, China

I. Introduction

The research and development of mobile autonomous robots has substantially expanded within the last decade and continues to increase due to the rapid advancement in theory and electronic technology. Different methods of how a robot can navigate through an unknown structured environment, that is to estimate its current position and orientation, have been developed. Robot platforms for simultaneous localisation and mapping (SLAM) include sensors systems such as motor encoders, optical vision, miniature radars [1], LIDAR and satellite positioning. The ubiquity of such sensors has attracted more and more researchers, who specialize in robotics, to develop their customized techniques in robotic simultaneous localization and mapping (SLAM).

References

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