Robust Landmark Estimation for SLAM in Dynamic Outdoor Environment

In this paper, we propose techniques which make SLAM accurate and practical in dynamic outdoor environment. In order to achieve the objective, stable feature detection by a laser range finder and efficient data management are introduced. The stable feature detection is used to select static and characteristic landmarks, it is possible to estimate every position of landmark accurately in the dynamic environment. The data management is introduced in order to decrease computational time and localization error of SLAM. Furthermore, for estimating landmark and robot’s position accurately, unscented transformation based sampling technique is also adopted. As a result, it is shown that the computing time and the maximum position error of SLAM decreases by 80% compared with typical landmark estimation, and position error of SLAM with unscented transformation becomes more accurate compared with FastSLAM2.0 in the dynamic outdoor environment.