| Title |
Objektų Pozicijos ir Orientacijos Nustatymo Metodų Mobiliam Robotui Efektyvumo Tyrimas / |
| Translation of Title |
Efficiency Analysis of Object Position and Orientation Detection Algorithms for Mobile Robot. |
| Authors |
Uktveris, Tomas |
| Full Text |
|
| Pages |
61 |
| Keywords [eng] |
pose estimation ; performance ; point cloud ; iterative closest point ; object detection |
| Abstract [eng] |
This work presents a performance analysis of the state-of-the-art computer vision algorithms for object detection and pose estimation. Initial field study showed that many algorithms for the given problem exist but still their combined comparison was lacking. In order to fill in the existing gap a software and hardware solution was created and the comparison of the most suitable methods for a robot system were done. The analysis consists of detector accuracy and runtime performance evaluation using simple and robust techniques. Object pose estimation via ICP algorithm and stereo vision Kinect depth sensor method was used in this work. A conducted two different stereo system analysis showed that Kinect achieves best runtime performance and its accuracy is 2-5 times more superior than a regular stereo setup. Object detection experiments showcased a maximum object detection accuracy of nearly 90% and speed of 15 fps for standard size VGA 640x480 resolution images. Accomplished object position and orientation estimation experiment using ICP method showed, that average absolute position and orientation detection error is respectively 3.4cm and 30 degrees while the runtime speed – 2 fps. Further optimization and data size minimization is necessary to achieve better efficiency on a resource limited mobile robot platform. The robot hardware system was also successfully implemented and tested in this work for object position and orientation detection. |
| Type |
Master thesis |
| Language |
Lithuanian |
| Publication date |
2014 |
