Research Repository

Kinect enabled Monte Carlo localisation for a robotic wheelchair

Theodoridis, T and Hu, H and McDonald-Maier, K and Gu, D (2013) Kinect enabled Monte Carlo localisation for a robotic wheelchair. In: UNSPECIFIED, ? - ?.


Download (433kB) | Preview


Proximity sensors and 2D vision methods have shown to work robustly in particle filter-based Monte Carlo Localisation (MCL). It would be interesting however to examine whether modern 3D vision sensors would be equally efficient for localising a robotic wheelchair with MCL. In this work, we introduce a visual Region Locator Descriptor, acquired from a 3D map using the Kinect sensor to conduct localisation. The descriptor segments the Kinect's depth map into a grid of 36 regions, where the depth of each column-cell is being used as a distance range for the measurement model of a particle filter. The experimental work concentrated on a comparison of three different localization cases. (a) an odometry model without MCL, (b) with MCL and sonar sensors only, (c) with MCL and the Kinect sensor only. The comparative study demonstrated the efficiency of a modern 3D depth sensor, such as the Kinect, which can be used reliably for wheelchair localisation. © 2013 Springer-Verlag.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Published proceedings: Advances in Intelligent Systems and Computing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 04 Dec 2014 16:06
Last Modified: 23 Jan 2019 00:16

Actions (login required)

View Item View Item