碩士 / 國立臺灣科技大學 / 機械工程系 / 101 / Indoor Robot Navigation System is the problem of integrating sensor measurements to determine the robot and landmarks location using SLAM technique. Indoor navigation in an unknown environment for a robot can be realized by using Visual SLAM technique, in which image sensor measurements taken at different locations with respect to various landmarks are integrated with other robot motion sensors to determine the robot and landmark positions simultaneously. This is typically implemented with expensive image sensors and dedicated hardware and required extensive computing power. In recent years, smartphones have become the portal devices for consumers to access Internet and cloud-based IT services under mobile computing environments. In current smartphone setting, only GPS as well as cellular network and Wi-Fi network are utilized to provide position information in outdoor and indoor locations with moderate accuracy. With digital camera and MEMS-based motion sensors becoming standard features and computing power being improved rapidly, smartphones have the potential to play key roles in realizing indoor robot navigation.
A smartphone-based positioning system suitable for indoor robot application is proposed in this research by integrating sensors available on a smartphone with wheel odometer feedback from a Bluetooth-connected robot vehicle. In this system, the smartphone is attached to a wheel-robot and the robot is performing only 2-dimensional planar motions in indoor home or office environments. The planar motion assumption greatly reduces the complexity of the navigation system equations. WiFi signal and the inertial sensors from the smartphone were considered to provide additional measurements for positioning calculations. But they were determined to be inadequate for the application in this thesis. The odometer sensors in the wheel-robot are used in the robot motion equations. For the phone camera measurements, the real-time performances of SIFT, SURF, and ORB feature detection and tracking algorithms are compared and one was chosen to implement on the smartphone. A state convergence analysis was conducted based on the result of a Visual SLAM experiment.
| Identifer | oai:union.ndltd.org:TW/101NTUS5489084 |
| Date | January 2013 |
| Creators | BUI QUANG HUY, BUI QUANG HUY |
| Contributors | Kao Wei-Wen, 高維文 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 74 |
Page generated in 0.02 seconds