SLAM solution based on particle filter with outliers filtering in dynamic environments

Our work presents a proposal for SLAM (Simultaneous Localization and Mapping) problem in dynamic environments. A method for robot self-localization is described. During operations in dynamic environments, only static landmarks are used. For this purpose, an outliers filter was designed. It separates static and mobile landmarks that are included in a set of neglectable marks. A modified particle filter is proposed which estimates the pose of a robot by a new way. Furthermore, it resamples a new set of particles at each estimative of the pose of the robot. Set of particles is based on current observations made by robot using RGBD camera. ROS (Robot Operating System) platform is used in order to integrate all hardware and our application.