Development of a fast self-localization algorithm based on laser range finders

This paper describes a new, fast self-localization algorithm based on laser range finders for use in the Intelligent Ground Vehicle Competition (IGVC) Auto-Nav Challenge. This challenge course uses circular cone shapes as obstacles; thus, we utilize this shape to achieve fast, real-time self-localization. To detect the accurate positions of circular cone obstacles, regardless of the robot´s observed direction, we apply the circular Hough transform. To robustly estimate the mobile robot´s posture, we formulate equations between the geometric relation and the traveling direction, and then solve these equations by applying singular value decomposition. To estimate fast and stable self-localization, we fuse the robot´s estimated posture and absolute position from GPS by applying a complex-type Kalman filter. The validity of the proposed algorithm is confirmed using actual outdoor environments.