1998 IEEE International Conference on Intelligent Vehicles 213

Transcription

1 Navigation by Integrating Iconic and GPS Information Shigang Li and Akira Hayashi Faculty of Information Sciences Hiroshima City University Asaminami-ku, Hiroshima, , Japan Abstract This paper proposes a method ofrobot navigation in outdoor environments by using GPS (Global Postioning System) information and panoramic views. Since an outdoor environment is very large scaled, it is very dicult to determine the global position based ona visual sensor. Although a global position can be obtained from a GPS navigator, it is dicult to locate a robot accurately since it has a distance error about meter. Our system is equipped with a GPS navigator and a camera. A coarse-to-ne method isused to generate an outdoor environment map and locate a mobile robot. First, a robot nds its approximate position based on the GPS information. Then, it identies its location from the image information. Experimental results in outdoor environments show the eectiveness of this method. 1 Introduction Making a map of environments and navigation by using the acquired map is an important problem for a mobile robot. Although many researches have been done so far, not so many researches cope with outdoor environments in contrast with indoor environments [6] [2] [5]. The scale of an outdoor environment isvery large. The data measured from a camera sensor is very noisy. Therefore, the qualitative methods by using landmarks are proposed. For example, viewpoint position is determined given relative visual angles between landmarks [7]; a distinct landmark which can be identied from very dierent points of view is used for recognition [1]. However, how to select landmarks from scenes is a problem. To cope with this problem, [8] proposes a datadriven method to describe route scenes by using panoramic views when a robot moves along a route. Further, the landmark with distinctive scene features can be selected automatically based upon the range and iconic cues of segmented objects along route [3], and a robot can recognize the route when it moves along the same route again by matching the memorized objects with incoming new scenes [4]. However, if an outdoor environment is very large and includes some similar scenes, it is dicult to identify its location in the environment only by using the visual cues. Even a human being may be lost in this situation. How a robot knows that it returns to the starting point from a visual sensor is a dicult problem, especially in a very-large-scale outdoor environment. On the other hand, the global position can be obtained simply from a GPS (Global Positioning System) navigator. This paper proposes a method of robot navigation in outdoor environments by using GPS (Global Postioning System) information and panoramic views. Since an outdoor environment is very large scaled, it is very dicult to determine the global position based on a visual sensor. Although a global position can be obtained from GPS information, it is dicult to locate a robot position accurately since it has a distance error about meter. Our system is equipped with a GPS navigator and a camera. A coarse-to-ne method is used to generate an outdoor environment map and locate a mobile robot. First, a robot nds its approximate position based on the GPS information. Then, it identies its location from the image information. An experimental result in outdoor environments shows the eectiveness of this method. 2 Our system Our system consists of a car, a GPS navigator and a camera. The experimental environment is the campus of Hiroshima City University which is shown in Figure. 1. The contours enclosing the area is the route used in our experiment. A human drives the car along the route in the direction of counter clockwise. Mean IEEE International Conference on Intelligent Vehicles 213

2 while the camera is mounted on the left side of the car and its optical axis is orthogonal with the car moving direction. Thus, we can acquire the GPS information and image sequences simultaneously when the car moves along a route. Height[m] E132 º 25.4 N34 º 26.4 N34 º 26.2 Latitude N34 º 26.0 N34 º25.8 E132 º 25.2 E132 º 25.0 Longitude E132 º 24.8 Figure 2: The 3D map of the route plotted by using the data of latitude, longitude and height. 2.2 Panoramic views Figure 1: The top view of a part of the campus of Hiroshima City University, where the contours enclosing the area is the route used in our experiment. Suppose a camera moving along a smooth path with its optical axis orthogonal to the moving direction. If we set a vertical slit with a single pixel width in an image and arranges 1D images through the slit along the time axis, we get a wide view of environment called a panoramic view [3]. Figure. 3 shows the panoramic view taken along the route, in which intersections are enclosed by rectangles. 2.1 GPS navigator The GPS system is fully operated and maintained by U.S. Department of Defence. The GPS navigator receives signals from a group of satellites in space to show the position anywhere in the world, 24 hours a day. So, it is very convenient to use it for navigation. The HG-7 type of GPS navigator made by Marine Electronics, Inc. is used in our experiment. The latitude, longitude, height and speed at a position can be acquired from the GPS navigator. Figure. 2 shows the 3D map of the route in Figure. 1, which is plotted by using the data of latitude, longitude and height. Since the GPS navigator has a distance error about meter, the 3D curve in Figure. 2 is not closed although the car returns to the starting point. Thus, it is dicult to use a GPS navigator to localize our car accurately. For example, it may be that you really have moved through an intersection, but from the GPS navigator data the intersection seems forward; as a result, you may fail to make a turn at the intersection. To cope with such problems, we use visual information taken from a camera. 3 Making environment map by fusing GPS information and panoramic views When the car runs along a route, we can acquire the GPS information and panoramic views simultaneously. We synchronize the two devices (the GPS navigaotr and the camera) according to time. By plotting the panoramic view along the route in terms of GPS information, we can obtain Figure. 4. Figure. 5 shows the view segments which is zoomed up at the starting point. Due to the position error from the GPS navigator, the same starting point is plotted at dierent positions. However, as shown in Figure. 5, the same scene is viewed again if the car returns to the starting point. Thus, we could correct the position error according to the iconic information of panoramic views. Our method is as follows. The car runs along a route and acquires the position information from a GPS navigator and a panoramic view from a camera simultaneously. When the car arrives at a position which is near to that passed before within the distance error of the GPS device, the incoming scene is compared with the memorized panoramic view IEEE International Conference on Intelligent Vehicles 214

3 Figure 3: The panoramic view taken along the route IEEE International Conference on Intelligent Vehicles 215

4 The Dynamic programming method is used to match the incoming scenes with the memorized panoramic view [4]. If the score of matching is high enough, we can know that the car return to the same position and fuse them to obtain a closed route. If the matching is failed, we can know that the car is runing another route which is near enough to that tranversed before. Here, we give more detailed explaination on the dynamic programming method. The objective is to match the memorized panoramic view segment with the incoming scenes. Instead of matching the whole memorized view segment, we sample the view segment and obtain a sequence of slit images, fs1;s2; :::; S j ; :::; S n g, which is shown on the lower left in Figure. 6. The upper right image in Figure. 6 is the new panoramic view taken from incoming scenes. We Figure 5: The view zoomed up at the starting point. compute of the similarity, C(S j ;P m;j ), between the slit image, S j, of the memorized view segment and the slit image, P m;j, of the new panoramic view by SSD method. Where n is the number of the sequence of slit images. P m;j is the slit image from the new incoming C(S j ;P m;j )= panoramic which is within the error range of the GPS Xq01 f( r 0 i;sj ri;pm;j T i=0 i;s j 0 ) 2 +( g 0 i;sj gi;pm;j Ti;pm;j 0 T i;s j 0 ) 2 +( b 0 i;sj bi;pm;j Ti;pm;j 0 T i;s j 0 ) 2 navigator. The bigger the error range of the GPS navigator is, the bigger m is. We carry it out by the dy- Ti;pm;j 0 g namic programming method [4]. Figure. 6 shows the Where, r i;s j, g i;s j, b i;s j are the R, G, B values of ith pixel at S j, respectively; ri;pm;j 0, g0 i;pm;j and b0 i;pm;j are matching result of memorized slit images with the incoming panoramic view when the car returns to the the R, G, B values of ith pixel at p m;j, respectively; T i;s j = r i;s j + g i;s j + b i;s j ;T0 i;pm;j = r0 i;pm;j + g0 i;pm;j + starting point. The matching result is shown in the lower right. b 0 i;pm;j ; q is the pixel number of the slit image. The closed route found after fusing GPS information and panoramic views is shown in Figure. 7. Fig- If the sequence of slit images, S1;S2; :::; S j ; :::; S n, appear in the new incoming panoramic with the same ure. 8 shows the view segments which is zoomed up order, the matching succeeds. We realize the matching at the starting point. We distribute the position error by minimizing the following evaluation function. along the route. nx We can use this map for car navigation. min f C(S j ;P m;j )g fpm;jg j=1 Figure 4: Plotting the panoramic view along the route in term of GPS information. Figure 7: The closed route found after fusing GPS information and panoramic views IEEE International Conference on Intelligent Vehicles 216

5 left in Figure. 10. Here the same dynamic programming method with Figure. 6 is used. Thus, the car can clear its position error accumulated so far when it identies such a location, and is expected to arrive its destination correctly. 5 Conclusions Figure 8: The view zoomed up at the starting point after the fusion. 4 Navigation based upon the built map Now we consider how a car makes its navigation based upon the built map. As a concrete task, let the car nd a given intersection. Figure. 9 shows the GPS information and the view segment at intersetion 7 in Figure. 3. GPS N W H283[m] Figure 9: The GPS information and the view segment at intersetion 7. Instead of matching the whole view segment with the incoming scene, we sample the view segment and obtain a sequence of slit images which is shown on the This paper proposes a method of robot (car) navigation in outdoor environments by using GPS (Global Postioning System) information and panoramic views. A coarse-to-ne method is used to generate an outdoor environment map and locate a mobile robot. First, a robot nds its approximate position based on the GPS information. Then, it identies its destination from the image information. An experimental result in outdoor environments shows the eectiveness of this method. References [1] D. Dai and D. T. Lawton, \Range-free qualitative navigation," Proc. of IEEE Int. Conf. on Robotics & Automation, pp , [2] J. Horn and G. Schmidt, \Continuous localization for long-range indoor navigation of mobile robots," Proc. of IEEE Int. Conf. on Robotics & Automation, pp , [3] S. Li and S. Tsuji, \Selecting distinctive scene features for landmarks", Proc. IEEE Int. Conf. Robotics & Automation, pp , [4] S. Li, S. Tsuji and A. Hayashi, \Qualitative Representation of Outdoor Environment along Route", Proc. ICPR, Vol.1, pp , [5] S. Li, J. Nagata and S. Tsuji, \A navigation system based upon panoramic representation," Proc. of IROS, Vol. 17, pp , [6] M. J. Mataric, \Integration of representation into goal-driven behavior-based robot," IEEE Tran. on Robotics & Automation, Vol. 8, No. 3, pp , [7] K. T. Sutherland and W. B. Thompson, \Inexact navigation," Proc. of IEEE Int. Conf. on Robotics & Automation, pp. 1-7, [8] J. Y. Zheng and S. Tsuji, \Panoramic representation for route recognition by a mobile robot", Int. J. Computer Vision,, 9:(1), pp , IEEE International Conference on Intelligent Vehicles 217

6 Figure 6: The matching result of memorized panoramic view with the incoming slit images when the car returns to the starting point. Figure 10: The matching result of a given intersection with the incoming scenes IEEE International Conference on Intelligent Vehicles 218