情報処理学会研究報告 IPSJ SIG Technical Report Vol.2015-CVIM-198 No /9/15 1,a) 1,b) 1,c) 1,d) 1,e) ADAS GPS 2 2 ICP GPS 1. (ADAS) ( ) ( )

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1 1,a) 1,b) 1,c) 1,d) 1,e) ADAS GPS 2 2 ICP GPS 1. (ADAS) ( ) ( ) a) b) c) d) e) [11] (MMS) GPS global pose map pose ( ) [1], [2], [5], [8], [10] ( ) (LRF)

2 lane marking detection area LRF fisheye camera 2 Sensor layout of our test car. The fisheye cameras are mounted on the door mirrors and the front grill. The LRFs are installed inside the fog lamp holes at the front bumper. 3 System diagram. Y (North) X (East) 1 High-definition map. Lane markings and curbs are shown by white lines and red lines, respectively X Y 3km 1 ( 1) LRF LRF (LKA) (LDW) 30 2

3 y H x w in (x,y) scan direction y skew x (x 1, y 1 ) (x 2, y 2 ) θ (x 0, y 0 ) H w out W 4 Template of lane marking detection. 5 Skew. Han [2] SLT[9] ESLT SLT (ESLT) 4 μ in,μ out σin 2,σ2 out () N in,n out (x, y) 2 S x,y,win σ 2 B S x,y,win = σb 2 +. (1) σ2 W σb 2 σ2 W σb 2 = N inn out (μ in μ out ) 2 (N in + N out ) 2, (2) σw 2 = N inσin 2 + N outσ2 2. (3) N in + N out 1 [0, 1] 2L W 4 x 1 x (x, y) w in θ 5 xy (x 0,y 0 ) (x 1,y 1 ) (x 2,y 2 ) x 2 =(y 0 y 1 )tanθ + x 1, (4) y 2 = y 1 (5) w in θ 4 5 y = y 0 τ cm [3], [4], [6] 40 LRF LRF LRF 6 LRF κ 3

4 curb L [m] ego vehicle 6 Point cloud from LRFs. 3.4 [7] X t+1 X Y t+1 = t v Y t + t cos θ t t v t sin θ t t (6) ω t t θ t+1 θ t SLAM ( 1 ) θ ( 2 ) (X t, Y t, θ t )( 7 ) θ Θ ( 3 ) L[m] L[m] 4. ( ) ICP (Iterative Closest Point) ICP point cloud (lane marking or curb) trajectory (history of odometory) L [m] 7 Map reconstruction proceedure. ICP 8 2 ICP ICP 2 (X, Y, θ) ( T [sec]) ICP 0.9T [sec] N 5. 3km 1 ( 1) 1m RTK-GPS 1 4m 1m 0.2[m/pixel] 4

5 start 空走補償 対応点探索 コスト最小 化 < 0.9T 経過時間 確認 > 0.9T 誤検出点の 削除 空走補償 end 図8 Flow chart of registration between the reconstructed map and the prior map. 表 1 Parameter configuration for the evaluation. パラメータ 値 地図復元用角度パラメータ (Θ) 20 [deg] 地図復元用距離パラメータ (L) 50 [m] ICP 周期 (T ) 0.1 [sec] 白線検出用俯瞰画像解像度 0.02 [m/pixel] 白線検出用分離度閾値 (τ ) 0.15 白線検出用テンプレート高さ (h) 50 [pixel] 白線検出用テンプレート注目領域幅 5, 6,..., 10 [pixel] 白線検出用テンプレート歪み角度 -30, -20,..., 30 [deg] 縁石検出用高さ閾値 (κ) 0.1 [m] 誤検出点判定用パラメータ (N ) 30 (3 [sec] 相当) の解像度で作成した (図 2) 車両進行方向に対して角度の ついた白線を検出するためには 10 度刻みで-30 度から+30 図 9 An example of map reconstruction. 度の 7 通りの歪み画像を作成した 縁石検出では縁石と判 断する局所的な高低差の閾値を 10cm とした 図 9 は 3.4 節で述べた手続きで走行軌跡に沿って地図を れている このように 必要以上の検出点を保持しないこ とで 計算コストの削減が実現されている 復元した様子を示している 図 9(上) は自車が第 1 のカー 図 10 は各時刻における自己位置推定結果と RTK-GPS ブに進入した時の復元地図であり カーブ手前の直線区間 の値の誤差を位置と姿勢角についてプロットしたものであ の検出点からなる 図 9(中) はカーブを抜け 次のカーブ る 図中の黄色い編みかけ部は RTK-GPS がうまく信号を に差し掛かったときの復元地図である このとき 自己位 受信できず位置精度が大きく低下した区間 緑色の編みか 置を拘束するためにカーブ直前の直線区間の検出点は保持 け部は車両がカーブしている区間をそれぞれ示している されているが カーブ直後の検出点は自己位置の拘束には 前述の通り 初期位置は誤差を含んでいるので走行開始直 不要なので削除されている様子が見て取れる 最後に 図 後の誤差は大きくなっているが 最初のカーブを曲がった 9(下) は第 2 のカーブを抜けた直後の様子である このと ところから徐々に誤差が減少していることがわかる また き 第 2 のカーブの前後の検出点のみで自己位置を拘束す カーブを曲がった後から次のカーブに差し掛かるまでの ることができるので 第 1 のカーブ周辺の検出点は削除さ 間 位置誤差が徐々に大きくなっていることがわかる こ 2015 Information Processing Society of Japan 5

6 position error [m] position angle time [sec] 10 Y [m] Estimation error. The errors are not shown, because the GPS didn t work in the yellow shaded areas. The green shaded areas show curves where the vehicle s orientation changed rapidly. estimate GPS GPS didn t work in these areas X [m] Estimated trajectory and ground truth from the RTK- GPS. Note that the GPS didn t work well in some areas due to poor signal quality RTK-GPS 11 RTK-GPS ( ) angle error [deg] 6. 10Hz [1] D. Gruyer, R. Belaroussi, and M. Revilloud, Map-aided localization with lateral perception, IEEE Intelligent Vehicles Symposium Proceedings, pp , Jun. 8 11, [2] S.J. Han and J. Choi, Real-Time Precision Vehicle Localization Using Numerical Maps, ETRI Journal, Vol. 36, No. 6, pp , [3],,,, Conformal Geometric Algebra,, PRMU , IBISML , [4],,,,,, Vol.28, No.5, pp , [5] J. Levinson, M. Montemerlo, and S. Thrun, Map-Based Precision Vehicle Localization in Urban Environments, Procedings of Robotics: Science and Systems (RSS), [6],,, (C ), Vol.77, No.782, pp , [7] S. Thrun, Probabilistic robotics, The MIT Press, [8] A. Schindler, Vehicle self-localization with highprecision digital maps, IEEE Intelligent Vehicles Symposium (IV), pp , Gold Coast, QLD, Jun , [9] T. Veit, J.P. Tarel, P. Nicolle, and P. Charbonnier, Evaluation of Road Marking Feature Extraction, IEEE Int. Conf. Intell. Transp. Syst., pp , Beijing, China, Oct , [10],,,,,,, Vol.45, No.3, pp , [11],, ViEW2014,

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