Open AccessOccupancy grid map algorithm with neural network using array of infrared sensors
Author(s) -
Nor Azlina Mohamad Yatim,
Norlida Buniyamin,
Zarina Mohd Noh,
Nur Aqilah Othman
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1502/1/012053
Subject(s) - occupancy grid mapping , occupancy , grid reference , computer science , grid , artificial neural network , algorithm , simultaneous localization and mapping , artificial intelligence , representation (politics) , robot , global map , object (grammar) , computer vision , mobile robot , mathematics , engineering , architectural engineering , politics , law , political science , geometry
Occupancy grid map is a map representation that shows the occupancy of spaces, whether there is any object in a particular area or it is a free space. This map representation is also commonly known as a grid map. However, the accuracy of the occupancy grid map is highly dependent on the accuracy of the sensors. In this paper, low cost and noisy sensors such as infrared sensors were used with the occupancy grid map algorithm integrated with a neural network. The neural network was used to interpret adjacent sensor measurements into cell’s occupancy value in the grid map. From the simulation experiments, it is observed that, that neural network-integrated algorithm has a better map estimate throughout robot’s navigation with mean of 28% more accurate compared to occupancy grid map algorithm without neural network. This finding is beneficial for implementation with simultaneous localization and mapping or commonly known as SLAM problem. This is because SLAM algorithm makes use of both estimations of environment’s map and robot’s state. Thus, a better map estimate throughout the robot’s journey can improve a robot’s state estimate as well.