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Mobile Robot Localization Using On-board and Off-board Laser Range Finders and Reflective Markers
| Content Provider | Semantic Scholar |
|---|---|
| Author | Carvalho, Henrique Cyrne Henrique |
| Copyright Year | 2017 |
| Abstract | The application of mobile robots is a reliable solution for autonomous transportation in industry, as well as in nuclear facilities to transport radioactive loads. However, the common solutions used in industry based on on-board sensors can be compromised by the exposure to activated loads. A redundant solution based on off-board sensors can improve the reliability of the robots localisation means. The sensors can be used independently as two separate systems, one on-board and other off-board, or they can be used together as single system. The addressed sensors are the Laser Range Finders and the landmarks are reflective markers, which can both be installed on the environment and on the vehicle. The focus of this work are the localisation algorithms and their performance. The addressed algorithms are: the Extended Kalman Filter, the Unscented Kalman Filter and the Monte Carlo Localisation, since they are the most used and available solutions in the literature. However, the novelty of this work is to combine these algorithms with on-board and off-board sensors. The motion model used is specific for a vehicle with two driving and steerable wheels, the developed method does not lose generality for other vehicles as long as they move in a two dimensional environment. The developed methods assume marker matching without error and the detection of markers with any incident angle of the laser. Simulation results were obtained for scenarios with a layout similar to the basement level of the International Thermonuclear Experimental Reactor. Nevertheless, the proposed solution can be also applied to industrial warehouse scenarios. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://fenix.tecnico.ulisboa.pt/downloadFile/1689244997258115/msc-thesis-extended.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
