Development of a Robust Indoor 3D SLAM Algorithm

Transcription

1 Develpment f a Rbust Indr 3D SLAM Algrithm Timthy Murphy Hnrs Tutrial Cllege Dr. David Chelberg Ohi University Schl f Electrical Engineering and Cmputer Science Russ Cllege f Engineering and Technlgy

2 Prblem: Intrductin Create Map during Explratin f Envirnment Must Knw Lcatin t Accurately Create Map Must have a map t accurately find lcatin. Applicatins Search and Rescue Hme Health Simultaneus Lcalizatin and Mapping (SLAM) Appearance Based SLAM Depth Based SLAM Turtlebt 2.0 Sensrs: Micrsft Kinect Inertial Unit Bump and Cliff Sensrs Laptp: Asus X200-CA Celern 1.3 GHz 4 GB RAM 320 GB Hard Drive frm

3 3D SLAM 3D SLAM Algrithms Appearance Based SLAM Use Clr Features Depth Based SLAM Uses Depth Features Our Algrithm Calculates Clr Features Cmbine Clr Features with Depth Infrmatin Find Matching Keypints f Clr and Depth Features Calculate Transfrmatin based n Keypints.

4 Outline f Algrithm Input: Sequential RGB-D Pint Cluds Output: 3D Pint Clud Aligned int single frame 3D_SLAM{ Cluds = {}; Keypints = {}; Descriptrs = {}; Transfrm = I while(!user.quit()){ clud = get_next_clud(); Cluds.append(clud); img = get_2d_image(clud); feature, keypint = get_2d_keypints(img); Keypints.append(keypint); Descriptrs.append(feature); if (length(cluds) > 1){ 2D_crr = get_2d_crrespndences(keypints[-1], Descriptrs[-1],Keypints[-2], Descriptrs[2]); 3D_crr = get_3d_crrespndences(2d_crr, Cluds[-1], Cluds[-2]); RANSAC(3D_crr); Transfrm = get_transfrm(3d_crr); Cluds[-1] *= Transfrm; } } }

5 Features Feature Extractin Image Structures: Pints, Edges, Crners, Objects Lcal prperties f an image Invariant t Translatin, Scale, Rtatin Imprtance Imprve Perfrmance Allws cmparisn f Images Shuld be invariant t Transfrms Surf Features (frm

6 SURF Features Speeded Up Rbust Features (SURF) OpenCV Lcal Feature Detectr and Keypint Finder Uses Hessian Blb Detectr Affine-invariant feature detectr based n secnd partial derivatives f image smthed using a Gaussian Kernel Surf Keypints 2D image (left) SURF Keypints shwn in 3D Crdinate Space (right).

7 NARF Features Nrmal Aligned Radial Features (NARF) PCL Lcal Feature Detectr and Keypint Finder Calculates change f nrmal arund pints f interest using depth image. SURF vs NARF 2D Image (left) Dwn Sampled Pint Clud & extracted NARF Features (right) SURF Feature Extractin takes Secnds NARF Feature Extractin takes Secnds SURF Features prduces mre Keypints that were mre stable.

8 Keypint Crrespndences Calculate Crrespnding Keypints OpenCV s Fast Library fr Apprximating Nearest Neighbrs (FLANN) Filter Results using Threshld All transfrms between images are Affine (Scale, Translatin, Rtatin) Distance between crrespnding keypints shuld be similar Reject any Crrespndences that are t far apart Remaining Keypints are Assciated int 3D Space Reverse Prcess f calculating 2D Image frm 3D pint Clud

9 SURF Keypint Crrespndences SURF Keypint Matches in 2D Image. Incming Image (left) Previus Image(right)

10 RANSAC Randm Sample Cnsensus (RANSAC) Iterative Methd fr estimating parameters f a Mathematical Mdel Select randm sample f data pints t fit mdel Test remaining pints if they fit the mdel. Prduces a set f Inliers and Outliers Inliers are the data pints that fit the mdel Outliers are the pints that d nt Try t maximize the set f Inliers Use RANSAC n 3D Crrespndences Reject the utlier set Remve prly matched Crrespndences frm

11 Transfrmatin Estimatin Levenberg Marquardt Transfrmatin Estimatin Nn-linear minimizatin f least-square cst functin Distance between crrespnding keypints Iterative methd fr finding Rigid Transfrm Estimatin Singular Value Decmpsitin Used t minimize least squares f cst functin Can have clsed frm slutin t find Rigid Transfrmatin Matrix

12 Further Imprvements Additinal Keypint types BRISK, SIFT, FAST keypints Use Multiple Keypint types Determine which keypint type wrks best in current envirnment Imprve Levenberg Marquardt methd Alter implementatin t allw t test fr cnvergence f pint cluds Calculate hw well aligned the resulting transfrmatin is Lp Clsure Allws the rbt t knw it has already seen the area befre Adds additinal cnstraints t the map t imprve errrs in map Increase rbustness, reliability and the quality f the generated Map fr future navigatin purpses.

13 Cnclusin Current results prduce a 3D Map that cmbines all input Pint Cluds. These Results can be imprved fr better perfrmance and accuracy f the Map.

14 Questins

15 References [1] Bay, H., Ess, A., Gl, L., Tuytelaars, T. SURF: Speeded Up Rbust Features. Cmputer Visin and Image Understanding (CVIU). (2008). Vl 110, N. 3, pgs [2] Cheeseman, P., Self, M., Smith, R. Estimating Uncertain Spatial Relatinships in Rbtics. Prceedings f the Secnd Annual Cnference f Uncertainty in Artificial Intelligence. (1986). University f Pennsylvania, Philadelphia, PA, USA: Elsevier. pgs [3] Fx, D., Henry, P., Herbst, E. Krainin, M., Ren, X. RGB-D Mapping: Using Depth Cameras fr Dense 3D Mdeling f Indr Envirnments. Web. Viewed 21 April [4] Flannery, B., Press, W., Teuklsky, S., Vetterling, W. Numerical Recipes 3 rd Editin: The Art f Scientific Cmputing. Clumbia Press. (September 2007). [5] Hung, D., Sun, C., Wang, Y. Imprving Data Assciatin in Rbt SLAM with Mncular Visin. Jurnal f Infrmatin Science and Engineering 27. (March 2011). pgs [6] Labbe, M., Michaud, F. Appearance-Based Lp Clsure Detectin fr Online Large-Scale and Lng-Term Operatin. IEEE Transactins n Rbtics. (June 2013). pgs [7] Lwe, D. G. Distinctive Image Features frm Scale-Invariant Keypints. Internatinal Jurnal f Cmputer Visin. (2004). Vl 60, N. 2, pgs [8] Miklajczyk, k., Schmid, C. An affine invariant interest pint detectr. Prceedings f the 8 th Internatinal Cnference n Cmputer Visin, Vancuver, Canada [9] Mirwski, P. Depth Camera SLAM n a Lw-cst WiFi Mapping Rbt. Web. Viewed 21 September [10] Descriptin f Turtlebt 2.0 can be fund at [11] Descriptin f Micrsft Kinect and hw it can be used can be fund at [12] Fr specifics n OpenCV, PCL, and ROS visit and respectively. [13]