Abstract
In recent years, simultaneous localization and mapping (SLAM) becomes an important topic for robotic research. The ability that an autonomous mobile robot can simultaneously locate itself and navigate in an unknown indoor environment is indispensable. 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. Many algorithms can be used to reduce error like Particle Filter, Kalman Filter and so on. In our system, we use Extended Kalman Filter to revise the system error of SLAM problem. In this thesis, we propose a system which extracts image line features from stereo camera as landmarks, and use stereo property to obtain the 3D vertical line landmarks. This system is based on the EKF. To handle with measurement uncertainty, we use two different observation models. One is nearby landmark model, the other one faraway landmark model. Our algorithm contains a wheeled robot, UBOT, which serves as our experiment platform, odometer data, image line segmentation, 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 in an environment lacking texture. The robot moves at a speed of 0.1m/s and simultaneously locates itself. The estimated error and computational time of this system are acceptable.