258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS ) GPS Global Positioning System

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1 Vol. 52 No (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver. On the probe-car, the system detects global position of roadside edge and road side hue information from the images taken by the camera. On the server side, the system integrates probed road shapes using DP matching based on hue information. The experimental results are shown that the proposition can generate accurate road map from plural road shapes detected by probe-cars. GPS GPS DP A Road Map Making Probe System by Integration of Road Shapes with Roadside Hue Information Hitoshi Yamauchi, 1 Akira Tomono 2, 1 and Akihiro Kanagawa 1 Recently, many car navigation systems are used. And the road map are used in the car navigation systems as a main information. However, the map data may not be refreshed and the contents is remained in old information. This is caused by huge cost for refreshing. For this problem, some researches are proposed. For example, a specific measurement car is employed for high accurate 1. PND Portable Navigation Device 3 1) Google Maps 2) 3),4) 1 Faculty of Computer Science and Systems Engineering, Okayama Prefectural University 2 Graduate School of Systems Engineering, Okayama Prefectural University 1 Presently with System Enterprise Co., Ltd. 257 c 2011 Information Processing Society of Japan

2 258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS ) GPS Global Positioning System

3 259 Google Maps 2) GPS 3),4) GPS 11) GPS 6) 2.2 GPS 8) GPS 1 Fig. 1 Tracking line and road edge point detection. 9),10) 2 1 GPS GPS 2 2

4 ) 10) ICP GPS Fig. 2 Position estimation of pictures using GPS interpolation. ICP Interactive Closest Point 12) ICP ICP 2.2 GPS GPS 9) 10)

5 261 Fig. 5 5 An example of getting road side information. 3 Fig. 3 The model of proposition for dynamic map making system. 4 Fig. 4 Road side region and hue sampling area. 4 m n h 0 h<360 i 1 i n H i { H i = h 360 } 360 (i 1) h< n n i (1) 3.3 RANSAC m 5 Hough

6 262 RANSAC RANdom SAmple Consensus 13) RANSAC RANSAC GPS GPS DP Dynamic Programming 14) DP 2 2 DP DP 6 DP Fig. 6 DP matching. w A A B A A = wa +(1 w)b (2) w (2) w

7 263 H A,i = wh A,i +(1 w)h B,i (3) 4. 1 Table 1 Experimental data. data :30 2,923 data :30 2,946 data :15 3,152 datar :30 2,888 1 GPS PointGrey Research Flea 1/3 CCD [pixel] IEEE FM22IR 2.2 [mm] 15 [fps] GPS [m] CEP Intel Core2 Duo 3.0 GHz 2GB RAM PC GPS [deg] [m] m =43 n = [m] RANSAC [m] w = [km] data1 data3 3 ICP [km/h] datar GPS GPS GPS data1 data3 7 7 GPS 8 Google Maps datar GPS GPS

8 264 7 data1 data3 9 data1 data3 Fig. 7 Road shapes of data1, data2 and data3. Fig. 9 Integration result of data1, data2 and data3. Fig. 8 8 data1 data3 GPS 1 GPS measurement result of data1, data2 and data3 (magnified, no.1) [m] datar 7 GPS data1 data2 1 data

9 265 Fig data1 data3 Integration result of data1, data2 and data3 (magnified). Fig data1 data3 GPS 2 GPS measurement result of data1, data2 and data3 (magnified, no.2). 15 [m] 9 10 ICP DP [m] 9 GPS 11 data1 data3 GPS GPS Table 2 2 Processing times for integration process. 1 [sec] 2 [sec] [sec] 1, , , (15 [m]) (30 [m]) (50 [m]) (100 [m]) ICP DP , [sec] 1.0 [sec]

10 [fps] data4 data6 data4 data6 data1 data4 data6 data4 data5 data ( 50480, ) GPS 12 data4 data6 Fig. 12 Road shapes of data4, data5 and data6. 13 data4 data6 GPS Fig. 13 GPS measurement result of data4, data5 and data6.

11 data5 data GPS 2 5. GPS RANSAC DP 1) (2006). 2) Google ( ). 3) ( ). 4) G-BOOK mx G-BOOK G-BOOK mx/technology/probe.asp ( ). 5) G-BOOK mx G-BOOK G-BOOK mx/technology/map.asp ( ). 6) MIRU2009 IS3-30, pp (2009). 7) D Vol.J92-D, No.2, pp (2009). 8) ITS , pp (2005). 9) GPS 8 IEEE 8th HISS pp (2006). 10) ITS , pp (2007). 11) ITS ITS ( ). 12) Besl, P.J. and McKay, N.D.: A Method for Registration of 3-D Shapes, IEEE Trans. PAMI, Vol.14, No.2, pp (1992). 13) Fischler, M. and Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Graphics and Image Processing, Vol.24, No.6, pp (1981). 14) DP

12 268 PRMU , pp (2006). ( ) ( ) ITS IEEE ITS OR