Research On Simultaneous Localization And Mapping For A Home Service Robot

With the improvement of aging society and the change of modern lifestyle, home service robots graduately enter people's lives, showing the huge market demand.In order to make home service robots practical application, the ability of autonomous navigation is the key issue, which is also the key technology to realize the real intelligence and mobility. Simultaneous Localization and Mapping (SLAM) of home service robot is the basis for navigation. On the one hand, localization relies on the accurate map information. On the other hand, map construction, in turn, depends on the precise pose information. They are contradict and related, thus must be considered simultaneously. Therefore, this dissertation researches on the SLAM problem of home service robots.This paper firstly studies the SLAM problem based on the range sensors. To due with the problem that the conventional Rao-Blackwellized particle filters based SLAM algorithm requires a large number of particles and that the frequent resampling might lead to the problem of particle impoverishment,an improved approach is proposed.It takes into account both the odometry and the observed information when computing the proposal distribution, resample according to the calculation of the effective sample number and adds some stochastic particles in order to maintain the diversity.Thus this novel method decreases the number of particles and is able to meet the requirement of consistence. Stimulation experimental results show that the proposed algorithm improves the computational performance as well as builds grid maps with higher accuracy.Secondly, this paper does researches on visual SLAM (vSLAM) problem for home service robots. One of the key issues in vSLAM is feature points extraction of visual images. In this paper, the Speeded Up Robust Features (SURF) algorithm is employed to solve the environment recognize problem of the home service robot, and the library based image matching using only monocular vision is realized. The robot learns about the environmental information from successfully image matching. The SURF algorithm is three times faster in calculating speed than the Scale Invariant Feature Transform (SIFT) algorithm, so it is better in real time performance. After recognizing the environment, the home service robot combines the visual information and the grid map constructed from range sensors and odometer, so as to generate an indoor environment map containing semantic information. This map is helpful for home service robots to achieve follow-up tasks, such as path planning, navigation, etc.