Abstract
For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.
Highlights
Wireless sensor networks (WSNs) grasp the potential of various new applications in the area of management and control
The simultaneous localization and mapping (SLAM) algorithm is proposed in two different methods such as SLAM with linear Kalman Filter (KF) and SLAM with Extended Kalman Filter (EKF)
SLAM with linear KF is implemented in five different methods such as the motionless robot with absolute measurement, moving vehicle with absolute measurement, a motionless robot with relative measurement, moving vehicle with relative measurement, and moving vehicle with relative measurement while the robot location is not detected
Summary
Wireless sensor networks (WSNs) grasp the potential of various new applications in the area of management and control. The authors consider the procedure of simultaneous localization and mapping (SLAM) For this purpose, a linear Kalman Filter (KF) with SLAM and Extended Kalman Filter (EKF) with SLAM are applied [3, 4]. In contrast to a laser rangefinder, currently, small, light, and affordable cameras can offer higher determination data and virtually unrestricted estimation series These cameras work as passive sensor nodes and, do not affect one Wireless Communications and Mobile Computing another while deploying in similar operation areas. By using a map, for example, a set of distinct landmarks, the robot can reorganize its localization error by reentering the known areas.
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