Unmanned vehicles being used more and more for tasks that need to be done in environ- ments that are hard to access, or dangerous for humans. Because the vehicles are unmanned they need some way of conveying information to the operator about where it is located. In some cases visual feedback to the operator might be enough, but in environments with low visibility other techniques are required. This thesis will address the issue of localization in an underwater environment by means of side-scan sonars and an inertial measurement unit (IMU). It will explore whether it is possible to localize a remotely operated vehicle (ROV) in a known environment by fusing data from the different sensors. A particle filter is applied to the translational motion of the ROV and an extended kalman filter is used to estimate the vehicles attitude. The focus of the thesis lies in statistical mod- eling and simulation of the ROV and its sensors rather than in validation and testing in the physical realm. Results show that a particle filter localization is plausible in environments given varied enough readings. For cases where measurements are similar, such as close to the floor of a pool the filter tends to diverge.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-119096 |
| Date | January 2014 |
| Creators | Ferm, Erik |
| Publisher | Linköpings universitet, Reglerteknik, Linköpings universitet, Tekniska fakulteten |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0023 seconds