| Summary: | 碩士 === 國立清華大學 === 電機工程學系 === 99 === Simultaneous localization and mapping (SLAM) becomes an ever important topic for robotic research. The ability that an autonomous mobile robot can simultaneously locate itself and navigate in an unknown indoor environment is prerequisite. The simplest localization method only uses the odometer to estimate the robot position and pose, but the accumulated error is growing with the execution time of the system. The Extended Kalman Filter (EKF) is often applied to revise the system error of SLAM problem.
In this thesis, we propose a system which extracts features form SR-3000 3D image data as landmarks, and combine with the depth information to obtain the landmark positions. This system is based on the EKF. It contains a wheeled robot, UBOT, which serves as our experiment platform, odometry data, Harris corner detection, landmark’s 3D position reconstruction, and Extended Kalman Filter.
Our system can use a single sensor to implement the EKF-based SLAM in real time. It can work without any camera calibration for calculating the depth information and alleviate the computational effort. The robot moves at a speed of 0.2m/s and simultaneously locates itself. The estimated error and computational time of this system are acceptable.
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