Mobile robot navigation using the range-weighted Hough transform

Accurate navigation of a mobile robot in cluttered rooms USing a range-measuring laser as a sensor has been achieved. To extract the directions and distances to the walls of the room the range-weighted Hough transform is used. The following experimental results are emphasized: The area around the robot is measured using an on-board scanning time-of-flight laser. The observations of the walls are extracted using the range-weighted Hough transform (RWHT) and the position of the robot is continuousb updated using an extended Kalman filter (EKF). The experimental tests presented concern navigation in one room at a time. This is useful as The robot extracts the walls of the surrounding room from the range measurements. The distances between parallel walls are estimated with a standard deviation smaller than 1 cm. It is possible to navigate the robot along any preselected trajectory in the room. One special case is navigation through an open door detected by the laser. The accuracy of the passage is 1 cm at a speed of 0.5 d s . The trajectory is perpendicular to the wall within 0.5 degrees in angle. When navigating through corridors, the accuracy is better than 1 cm at 0.8 m / s the maximum speed of the robot. Odometric data and laser measurements are combined a proof of concepts, and makes the result clearer and easier to understand. A complete map-building and navigation system along these lines is under development with preliminary results given in [ 141. Different Sensor Systems and Related Work Robot navigation is generally based on a combination of internal sensors (like odometers and rate gyros) for dead reckoning and external sensors (like time-of-flight lasers) for finding objects in the surroundings of the robot and for locating external position references. Robot navigation is a wide research area with numerous contributions including books like [6] and [ 151. Navigation can use external references and landmarks occurring naturally in the environment like walls and tree trunks. Landmarks can also be artificial as in navigation using using the extended Kalman filter. The size of the cluttered rectangular room and the position and orientation (pose) of the robot are estimated during motion. The extraction and the result ing navigation are very robust against both spurious measurements in the laser measurements and disturbing objects. INTRODUCTION A method for navigating a mobile robot in cluttered rooms and corridors is introduced and tested using the robot in Fig. 1. The localization of the robot is performed relative to the walls of an indoor environment. radio beacons (including GPS) and, for indoor navigation, retroreflective tape as in [ll]. To put this contribution in its proper context some of the sensing principles used for mobile robot navigation are outlined in Table 1. The first column is for the scanning time-offlight laser used in this paper. The second column is for a typical ultrasonic system. The last column is for a navigation system where a rotating laser measures angles to several identical beacons Johun Forsberg, UfLdzrssonandhe Wernersson;Robotics &Automation, Luled Uniuersity of Technology, S-97187 tive tape. Luled, Sweden. E-mail: [email protected]. Actual navigation experiments using ultrasonics have been carried out in several laboratories (see [4] Of stripes Of retroreflec18 . /€E Robotics &Automation Magazine March 1995 Table 1 Typical Sensors for Mobile Robot Navigation.