Geometric Feature Based Rao-Blackwellized Particle Filter SLAM Using RGB-D Depth Images
| dc.contributor.advisor | Leung, Henry | |
| dc.contributor.author | Brahmanage, Gayan Sampath | |
| dc.contributor.committeemember | Macnab, Chris | |
| dc.contributor.committeemember | Ramirez-Serrano, Alejandro | |
| dc.date | 2018-06 | |
| dc.date.accessioned | 2018-01-22T16:44:24Z | |
| dc.date.available | 2018-01-22T16:44:24Z | |
| dc.date.issued | 2018-01-18 | |
| dc.description.abstract | In recent years, commercially available mobile robots that operate in indoor environments have become more popular in household, office environment and industrial settings. These robots are used to provide services autonomously such as cleaning and surveillance. Simultaneous Localization And Mapping (SLAM) is a framework that can assist robots to navigate autonomously. SLAM builds the map of the environment where the robot operates and simultaneously localizes the robot in that environment. To develop SLAM, many approaches have emerged using high precision and long-range sensors such as laser scanners. However, laser scanners are very expensive and provide no visual information. In this thesis, novel approaches to build maps using low cost RGB-D sensors in indoor environments are investigated. A goal of this thesis is to consider a major limitation on the hardware side of commercially available low-cost robots, namely processing power and available memory. A new approach is developed for RGB-D cameras that captures 2D maps by processing reduced amount of data acquired from RGB-D data frames. This approach uses distinct geometric features that are extracted from depth data in a 2D plane. Further, the performances of grid-based mapping is exploited by replacing laser scanners with RGB-D camera. Since RGB-D cameras provide both color and depth data, it is possible to fuse these two data frames to exploit the advantages over using laser scanners. The stored RGB images at each pose can be used to add additional capabilities to 2D SLAM such as object recognition. In this context, this work is further extended to construct a 3D map of the environment using estimated 2D poses and stored sequence of RGB-D data frames using RGB features. Methods are evaluated for accuracy and consistency using experimental data gathered from real environments. Also, the proposed approach is implemented to build 2D maps in real time on a low-cost robot. | en_US |
| dc.identifier.citation | Brahmanage, G. S. (2018). Geometric Feature Based Rao-Blackwellized Particle Filter SLAM Using RGB-D Depth Images (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/5375 | |
| dc.identifier.uri | http://hdl.handle.net/1880/106294 | |
| dc.language.iso | en | en_US |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject | SLAM | en_US |
| dc.subject | RGB-D camera | en_US |
| dc.subject | Features | en_US |
| dc.subject.classification | Education--Technology | en_US |
| dc.subject.classification | Robotics | en_US |
| dc.title | Geometric Feature Based Rao-Blackwellized Particle Filter SLAM Using RGB-D Depth Images | en_US |
| dc.type | master thesis | en_US |
| thesis.degree.discipline | Engineering – Electrical & Computer | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Master of Science (MSc) | en_US |
| ucalgary.item.requestcopy | true | |
| ucalgary.thesis.checklist | I confirm that I have submitted all of the required forms to Faculty of Graduate Studies. | en_US |