Abstract
In this study, a statistical estimation algorithm is developed to solve the SLAM (simultaneous localization and map building) problem, by using a robot equipped with only simple and cheap sensors. During map building and simultaneous localization, the robot can sense its environment with infrared sensors and can decide the path to follow by using the developed SLAM algorithm. The most frequent problems in SLAM algorithms are sensors' noise and odometry errors. To solve this problem, sequential Monte Carlo (SMC) method which is a well known particle filter application is used and promising results were obtained for the SLAM problem.
Keywords
Particle filter
Monte Carlo localization
Simultaneous localization and mapping
Odometry
Monte Carlo method
Computer science
Robot
Algorithm
Noise (video)
Artificial intelligence
Computer vision
Path (computing)
Mobile robot
Kalman filter
Mathematics
Subject Areas
Robotics and Sensor-Based Localization
·Aerospace Engineering ·Physical Sciences
Indoor and Outdoor Localization Technologies
·Electrical and Electronic Engineering ·Physical Sciences
Target Tracking and Data Fusion in Sensor Networks
·Artificial Intelligence ·Physical Sciences
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