IPSJ SIG Technical Report Vol.2012-ICS-167 No /3/ ,,., 3, 3., 3, 3. Automatic 3D Map Generation by Using a Small Unmanned Vehicle
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1 ,,., 3, 3., 3, 3. Automatic 3D Map Generation by Using a Small Unmanned Vehicle Hiroki Osaki, 1 Ken Watanabe 2 and Katashi Nagao 2 While 3D maps are useful to visualize complicated shapes of objects such as buildings, it is very costly to create and maintain them. We developed a mechanism to automatically generate indoor 3D maps by using a Small Unmanned Vehicle (SUV, for short). SUV is capable of autonomous run and collection of 3D data captured by an RGB-D (RGB and Depth) camera. SUV also has a function of generation of a 2D environmental map for localization of current position. Our method can make a precise alignment between the 3D maps and the 2D environmental maps so that our generated 3D maps can be applied to highly informative indoor navigations. 1 Department of Electrical and Electronic Engineering and Information Engineering, School of Engineering, Nagoya University 2 Graduate School of Information Science, Nagoya University 3, 2, 2 3., 3, 3,.,, 2 3., 3, 3. 3, 1), 2),, 3., RGB-D, 3 3 RGBD-ICP 3)., RGB-D, 3, 3. 2, 2 3,., (SUV: Small Unmanned Vehicle), 3. SUV,. SUV, 2,., SUV RGB-D,,.,, RGBD-ICP 3,, (SUV) SUV,,,,,. 1 c 2012 Information Processing Society of Japan
2 SUV 1 irobot Create 1 PC RGB-D. SUV,,. SUV RGB-D Kinect 2. Kinect RGB Fig. 3 Example of occupancy grid map 2.2, SUV., 2,, a t = (x t, y t, θ t ). x, y θ. SUV,,,. 4, 1 SUV Fig. 1 Configuration of SUV Fig Reconstruction of 3D shape SUV 2. SUV 1 irobot Create irobot Corporation. 2 Kinect Microsoft Corporation. Fig. 4 4 Trajectories of without localization and with localization 2 c 2012 Information Processing Society of Japan
3 ., Fig Distance between initial location and current location 3, 4. ( 1 ) ( 2 ) ( 3 ) ( 4 ) 3 (1) SUV, SUV (2) (1) SUV, RGB-D. (3) 3. 3,, ,., (4), ,.,,.,,,,., ,. 6.,. k-d.. A, 6. A,.,,. 6, B. A B, C. C.., 2. A C, B C, 7.,,,. 6 7 Fig. 6 State generated by the node Fig. 7 Graph structure between the nodes 3 c 2012 Information Processing Society of Japan
4 3.1.2 ( 1 ) A C ( 2 ) 1 R R i(0 i n) N m, N m, R i N m., N j = R i. ( 3 ) R R, R i N m, N m 2 R. R i + 1 R., R i 1 R 1 R 0. ( 4 ) 3 ( 5 ) C 6 ( 6 ) R R C B R. R R R R n 3.. ( a ) R n B R 0 n R, R i R. ( b ) R n C R n 2-4 R. R 0 n R, R n R. R i R. ( c ) R n C R ij, R 0 n R j, R i R., R i j R ij, R j = R n. ( 7 ) R R 8. 8 Fig. 8 Example of route SUV,,., SUV x,y ( θ y ), 3, 2 3,., SUV,.,, z ( θ p, θ r) SUV 3. RGB-D., ax + by + cz 1 = 0, RANSAC (Random Sample Consensus) a, b, c. 3, 3 a, b, c., 3.,.,.,. 4 c 2012 Information Processing Society of Japan
5 1000, , p p p = R p + T R T. R T ,, 3. A. A SUV. x, y, z R x, R y, R z. A = TR z (θ y )R x (θ p )R y (θ r ), SUV z θ p θ r SUV xy θ y. ( 1 ) F s, M s, P s. F s SURF (Speeded Up Robust Features) RGB,. M s z SUV. P s ( 2 ),.. ( 3 ) 1 F s,m s,p s, A f, A m, A p. A f. f s f t f s F s., p, t t p. A m 3 t m s m t. A p t p s p t. M s P s k-d,. ( 4 ) A f, A m, A p. A f RANSAC,. A m A p., A p p t, 3. ( 5 ) A f 2 2 A p A m 2.. p t m t. α = 0.3, β = 0.3, γ = e = α( w i t(fs) i ft i 2 1 ) + β( w j (t(p j s) p j A f A t) n j 2 ) p i A f j A p 1 +γ( w k (t(m k s) m k t ) n k 2 ) (α + β + γ = 1) A m k A m w:, t:, n: Levenberg-Marquardt 4),5) ,, , 3 SUV c 2012 Information Processing Society of Japan
6 情報処理学会研究報告 図 9 生成された 3 次元地図 Fig. 9 Example of 3D map 図 11 ポリゴン化した 3 次元地図 Fig. 11 Example of 3D polygon map 図 10 環境地図と生成された 3 次元地図の対応付け Fig. 10 3D map overlaid on a environmental map 4. お わ り に 次元地図のポリゴン化 前節で生成した 3 次元地図は膨大な数の点の集合であり, データ量が非常に大きく扱うの 本研究では, 自律走行可能な小型無人移動体 SUV に RGB-D カメラを搭載し, データ収 が困難である. そこで, 建物の内装を構成する主な要素である壁や床といった平面に着目し, 集経路を生成することで自動的にデータの収集を行い, 収集したデータから環境地図と対応 点の集合である 3 次元地図から平面を抽出しポリゴン化することで 3 次元地図の構造の単 の取れた 3 次元地図を生成する仕組みを実現した. これにより, 既存の位置推定の仕組みを 純化を行う. 3 次元地図全体から直接面を抽出することは困難であるため, 一つ一つのセン そのまま利用して, 3 次元地図をナビゲーションシステムなどに応用する事ができる. サーデータから面を抽出し, 抽出した面の重ね合わせを行うことで 3 次元地図のポリゴン化 本研究では, 平面だけを抽出しポリゴン化を行ったが, 実際の環境では机やいすなどのオブ を行う. 面の検出は 項の床の検出と同様のアルゴリズムで行う. ただし, ここでは面 ジェクトが存在する事が考えられるため, それらの 3 次元オブジェクトのポリゴン化を行う の向きを指定しない. また, 複数の面の検出するため, 点群から検出された面に含まれる点 必要がある. また, 生成した 3 次元地図を利用した応用システムの開発が今後の課題である. を取り除き, 面が検出されなくなるまで繰り返し面の検出を行う. ポリゴンを描画するため, 参 面のテクスチャを生成し描画範囲を指定する必要がある. そこで, 面に含まれる点を平面上 考 文 献 1) Thrun, S., Thayer, S., Whittaker, W., Baker, C., Burgard, W., Ferguson, D., Hahnel, D., Montemerlo, D., Morris, A., Omohundro, Z., Reverte, C. and Whittaker, W.: Autonomous exploration and mapping of abandoned mines, IEEE Robotics and Automation Magazine, Vol.11, No.4, pp (2004). 2) J., D.A.: MonoSLAM : real-time single camera SLAM, IEEE Trans. Pattern Anal. Mach. Intell., Vol.6, pp (2007). 3) Henry, P., Krainin, M., Herbst, E., Ren, X. and Fox, D.: RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments, Proc. of the ISER (2010). 4) Levenberg, K.: A method for the solution of certain non-linear problems in least squares, Quarterly Journal of Applied Mathmatics, Vol.2, No.2, pp (1944). 5) Marquardt, D.W.: An Algorithm for Least-Squares Estimation of Nonlinear Parameters, SIAM J. on Applied Mathematics, Vol.11, pp (1962). にマッピングし画像の平滑化を行うことでテクスチャ画像を生成し, その輪郭線と凸包を求 めることで描画範囲を指定する. 面の重ね合わせを行うため, 面の法線の向きと重心の位置 から一致する面を検出し, 一致する面が存在する場合には, 入力面と一致した面の法線ベク トルをそれぞれの面の面積で重み付けした平均を求め, それを重ね合わせてできる面の法線 ベクトルとする. 入力面と一致した面からそれぞれ基準となる 3 点を重ね合わせてできる面 上にマッピングし, テクスチャ上の 2 次元アフィン変換を求めることで新しいテクスチャを 生成する. 図 9 の点群から成る 3 次元地図をポリゴン化した地図を図 11 に示す. 点群から成る 3 次 元地図は約 1500 万点の頂点データから構成され, 約 800MB のデータサイズであったのに 対し, ポリゴン化した 3 次元地図では面の枚数が 34 枚, データサイズは約 30MB となりか なり単純化することができた. 6 c 2012 Information Processing Society of Japan
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