Kazumichi Inoue

Monte Carlo Localization using magnetic sensor and LIDAR for real world navigation

For realizing more stable outdoor navigation for mobile robots, this paper proposes a localization method using a magnetic sensor and a Light Detection and Ranging (LIDAR). In the proposed method, Monte Carlo Localization (MCL) using the LIDAR and a determination method of a heading direction using the ambient magnetic field are combined. In other words, the proposal distribution becomes dense at the true state of the robot by using the ambient magnetic field. The determination method is based on the advantage of the magnetic navigation proposed by us. By the proposed method, the robot enabled to navigate with accuracy in the outdoor environment, since the robust localization is realized. The effectiveness of the proposed method is shown through experiments. Moreover, two robots implemented the proposed method achieved the task of Real World Robot Challenge 2012. This means that the proposed method is effective for real world navigation.

Development of a Mobile Robot to Run in Tsukuba Challenge 2010

Abstract This paper describes an implementation of a mobile robot system in a 1 km autonomous mobile robot navigation challenge called ‘Tsukuba Challenge 2010’ which was held in the city of Tsukuba, Ibaraki, Japan. The mobile robot used only three types of sensors which were a magnetic sensor, two wheel encoders, and a laser range finder. For a stable navigation, we have proposed a method that is able to localize and perform trajectory tracking based on patterns of the magnetic fields intensity that occurs in the environment. It was divided into two distinct parts of map building and navigating. In the map building, the mobile robot was operated by a joystick along the course of the challenge to record the environmental magnetic field to build a map. The mobile robot then navigated by using a trajectory tracking method based on the map. During the seven trials which were given before the qualifying and final runs, the mobile robot completed the full course three times with once in small rain.