Research On Localization And Map-building Of Monocular Mobile Robot

With the development of computer science, sensor technology, artificial intelligence and the improvement of manufacture level, the robotics increasingly tends toward automation. Two of the essential problems to realize the automation of mobile robots are self-localization and map building. This is the foundation of autonomous navigation and environment exploring for mobile robots. The precision of localization and map is the key problem of whether mobile robots can be successfully applied in real environments or not. The intention of this dissertation is to describe the research on the localization and map building for monocular mobile robot in unknown environments. It principally introduced the algorithm of localization, the algorithm of SLAM based on particle filter, and the application of the algorithms to a mobile robot. The main contributions and innovation of this dissertation are as following:1) A new method of obtaining information from monocular camera called visual protractor is proposed in this dissertation. The information got from the visual protractor is the visual angle between two environment features. The visual angle has such a characteristic as invariability which does not relate to the pose of a robot, but merely to the robot's location. This characteristic is very helpful for robot's position tracking. The visual protractor provides a new method to use monocular camera for mobile robots localization.2) A algorithm based on EKF to correct the track of a robot has been presented. When there are only monocular camera and encoders in the robot for localization, the localization algorithm by fusing the information from above two sensors is described in this dissertation. The rough estimation of robot's position is obtained with the encoders. The monocular camera is used as visual protractor to get environment's information with which the algorithm corrects the primary position by EKF. The algorithm avoids calculating the coordinates of environment's features, so it can well satisfy the real-time performance of the robot localization. The experiment showed the localization precision was improved by the algorithm.3) A triangulation localization algorithm with visual protractor also proposed in this dissertation. Under the situation that the robot can get its orientation independently, a new localization algorithm based on triangulation with visual protractor is presented. On the basis of calculated coordinates of environments' land-marks, the optimal estimation of robot's position can be obtained from the algorithm with the stable information from visual protractor. The algorithm is analyzed in detail and an upper limit expression is deduced, so the reliability of the algorithm is guarantied theoretically. This algorithm was testified by the experiment of robot navigation. 4) To the SLAM problem for a miniature robot in unknown environment, an improved SLAM method on particle filter is provided. The coordinates of land-marks are estimated imprecisely with the data from camera and encoders. On the basis of general particle filter, the state vector is adjusted to make the high dimension calculation become a few times of low dimension calculation. So the precise estimation of robot's position is acquired by averaging the position estimations from particle filter. The improved algorithm reduced the cost of computation. The result of simulation experiment showed that the algorithm not only improved the precision of localization but also built a more accurate map.5) The SLAM algorithm has been testified on the lunar rover for principle demonstration and it made the robot realize localization and map building in an unstructured environment. According to the characteristic of the lunar rover's movement structure, both the state equation and the extent of error enlarged by the hypothesis that the robot moved on a plane were adjusted. The algorithm was validated with navigation of the lunar rover. The experiment showed that the environment map expressed with feature points was obtained from the algorithm and the error of localization reduced to one third. The environment map accorded with the real environment by Compare between them.