Open AccessImproved KNN Scan Matching for Local Map Classification in Mobile Robot Localisation Application
Author(s) -
Marni Azira Markom,
Abdul Hamid Adom,
Shazmin Aniza Abdul Shukor,
Norasmadi Abdul Rahim,
Erdy Sulino Mohd Muslim Tan,
Bukhari Ilias
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/557/1/012019
Subject(s) - iterative closest point , computer science , artificial intelligence , matching (statistics) , mobile robot , computer vision , overshoot (microwave communication) , process (computing) , point (geometry) , pattern recognition (psychology) , robot , point cloud , mathematics , statistics , telecommunications , geometry , operating system
Localisation is essential for autonomous mobile robot system enabling it to locate itself within its environment. One method to perform localisation is to use scan matching with iteration closest point (ICP) algorithm. However, typical ICP may be prone to inaccuracies in localisation and mapping due to problems associated with laser range data limitation such as overshoot data and blank data. This paper presents the improvement to the above problem by the inclusion of a threshold to the KNN scan matching algorithm during iteration process. The threshold is a percentage of nearest point of incoming input with respected to reference point. Threshold values of 0%, 70% and 90% were tested, and improvements of the classification performance were observed with the increase in the threshold values, with the latter achieving 100% accuracy. This work shows that the use of threshold in scan matching may improve the accuracy of local map classification.