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Active SLAM : a Framework My, on-going, PhD Research

Active SLAM : a Framework My, on-going, PhD Research. Henry Carrillo Lindado Advised by : José A. Castellanos. Bio – Academic Background. Name: Henry David Carrillo Lindado. Hometown : Barranquilla – Colombia. Academic: PhD in Computer Science and System Engineering (2010 -2014 )

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Active SLAM : a Framework My, on-going, PhD Research

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  1. Active SLAM : a Framework My, on-going, PhD Research Henry Carrillo Lindado Advised by: José A. Castellanos

  2. Bio – Academic Background • Name: Henry David Carrillo Lindado. • Hometown: Barranquilla – Colombia. • Academic: • PhD in Computer Science and System Engineering (2010 -2014) • University of Zaragoza - Spain • M.Sc. in Computer Science and System Engineering • M.Sc. in Electronics Engineering • B.Eng. in Electronics Engineering • Funding: FPI scholarship by the Ministry of Science and Innovation of Spain. 2010-2014. • Contact: • Here: 0.59 Cartesium • hcarri@unizar.es • http://webdiis.unizar.es/~hcarri/pmwiki/pmwiki.php 1

  3. So, What is my PhD about? • Objective:To build an active SLAM framework. • Why?: • Where should I go in order to improve my localization and map representation? • If I go from A to B, will I be lost (e.g. Unable to localize)? • What movements should I make in order to keep my metrical error below X mm? • Aim at: • Metrical representations. • Topological representations. • Metrical+Topologicalrepresentations. 2

  4. Whathave I done? Metrical 3

  5. Preliminaries – SLAM • H0:A model of the operative environment is an essential requirement for an autonomous mobile robot. • Three basic tasks: • Where am I? • What does the world look like? • Where do I go? • SLAM => Joint of two tasks. • SLAM => Does not define the path-trajectory of the robot. • Integrated approach => On the way to autonomy. 4Exploration and Mapping with Mobile Robots. CyrillStachniss. 2006.

  6. Preliminaries – Active SLAM (I) • Active SLAM => To integrate path planning into a SLAM process. • To explorer more area. • Navigate safely. • Reduce uncertainty. • Algorithms • 1º Alg. [Feder, Leonard](99) • Active perception [Bajacksy](86) • Infinite Horizon and MPC [Leung, Dissanayake](06) 5

  7. Preliminaries – Active SLAM (II) • Pseudo-code: • Set of trajectories • Assign a score to each trajectory • Uncertainty of map+robot • Trajectory constraints • Execute the trajectory with the optimum . 6

  8. Preliminaries – Active SLAM (II) • Pseudo-code: • Set of trajectories • Assign a score to each trajectory • Uncertainty of map+robot • Trajectory constraints • Execute the trajectory with the optimum . 6

  9. Preliminaries – Active SLAM (II) • Pseudo-code: • Set of trajectories • Assign a score to each trajectory • Uncertainty of map+robot • Trajectory constraints • Execute the trajectory with the optimum . 6

  10. Preliminaries – Active SLAM (II) • Pseudo-code: • Set of trajectories • Assign a score to each trajectory • Uncertainty of map+robot • Trajectory constraints • Execute the trajectory with the optimum . 6

  11. Uncertainty Criteria for Active SLAM (I) • Uncertainty/Inform. Criteria => • In the TOED, a design (i.e.), isbetterthan a design, if: • The above does not allow to quantify the improvement, therefore is desirable to: • It permits to quantify the uncertainty in . • Theory of Optimal Experiment Design (A-opt, D-opt, E-opt…). • Information Theory ( Entropy, MI…). 7

  12. Uncertainty Criteria for Active SLAM (II) • Some possible uncertainty criteria for active SLAM are: • Previous works ([Simand Roy, 2005], [Mihaylovaand De Schutter, 2003]) report A-opt as the best criterion and that D-opt gives null values. • A-opt, widely used:[Kollar2008] [MartinezCantin2008] [Meger2008] [Dissanayake2006]. • Although D-opt is commonly used in the TOED because it is optimal. Trace (A-opt) Max(E-opt) Determinant (D-opt) 8

  13. Uncertainty Criteria for Active SLAM (III) • It is indeed possible to use D-opt in the Active SLAM context: • The structure of the problem needs to be taken into account (i.e. The covariance matrix varies with time). • It is not informative to compare the determinant of a matrix lx lwith a mx m. • det(l x l) is homogeneous of grade l. • The computation of the determinant of a highly correlated matrix(e.g. SLAM) is prone to round-off errors. • Processing in the logarithm space • D-opt for a l x l covariance matrix: • Stem from [Kiefer, 1974] : 9

  14. Firstexperiment • Firstexperiment: on the computation • Is it possible to compute D-opt from a robot doing SLAM? • Execute a SLAM algorithm (e.g. EKF-SLAM, iSAM). • Compute in each step: A-opt, E-opt , D-opt, Determinant, entropy and mutual Information. • Simulated Robot indoor environment : MRPT/C++ • Real Robot indoor environment : Pioneer 3 DX - Ad-hoc • Real Robot indoor environment : DLR dataset • Real Robot outdoor environment : Victoria Park dataset 10

  15. 1E - Simulated Robot indoor environment (I) Scenario: • Area of 25x25 m • 2D EKF-SLAM • Sensor: Odometry + Camera(360º - 3m range) • 180 landmarks- DA Known. • Gaussian errors: Odometry + Sensors 11

  16. 1E-Simulated Robot indoor environment (II) Qualitative results (a)-(f) A-opt, E-opt, D-opt, determinant, entropy and MI. 12

  17. 1E-Real Robot indoor environment @ DLR Scenario: • Area60x40 m • Sensor: Odometry + Camera • 2D EKF-SLAM • 576 landmarks – DA known. 13

  18. 1E-Real Robot indoor environment @ DLRQualitative results (a)-(f) A-opt, E-opt, D-opt, determinant, entropy and MI. 14

  19. Firstexperiment – Quantitative analysis • Average correlation between the uncertainty criteria: • Variance: A-E (0,0002) / A-D (0,0540) / D-E (0,0481). • A-opt y E-opt=> High correlation. • E-opt is guided by a single eigenvalue. • A-opt y D-opt => Medium correlation. • H0: D-opt take into account more components than A-opt. 15

  20. Second Experiment • Second experiment: Active SLAM • What is the effect of the uncertainty criteria in active SLAM? • Active SLAM => Unitary horizon (greedy). • Uncertainty criteria => A-opt, D-opt and Entropy. • Effect =>MSE y . • Simulated Robot with unitary horizon: MRPT / C++ 16

  21. 2E-Simulated Robot indoor environment (I) Scenario: • Area of 20x20m and 30x30m • 2D EKF-SLAM • Sensor: Odometry + Camera (360º - 3m range) • Gaussian errors: Odometry + sensors. • Path planner: Discrete (A*) and continuous (Attract-Repel). 17

  22. 2E-Simulated Robot indoor environment (II) • Resulting paths for each uncertainty criterion: (a) D-opt, (b) A-opt y (c) Entropy. Each colour represents an executed path. 20 x 20 m map. • Qualitativeanalysis 18

  23. 2E-Simulated Robot indoor environment (III) • Resulting trajectories for 10000 stepsactiveSLAMsimulation. (a). Initial trajectory. (b) A-opt. (c). D-opt. • Qualitative analysis. 19

  24. 2E – Quantitative Analysis 30x30 m • Evolution of MSE ((a)-(c)) y chi2 ((d)-(f)) ratio. Average of 10 MC simulations. 20

  25. Take home message • D-opt is the optimum criterion to measure uncertainty according to the TOED (i.e. better than A-opt (Trace)). • It is possible to obtain useful information regarding the uncertainty of a SLAM process with D-opt. • D-opt shows better performance than A-opt in our simulated experiments of active SLAM. • To compute D-opt in the context of a SLAM process => use the formulation presented here. 21

  26. Whathave I done? Metrical: an example using D-opt 22

  27. FaMUS: Fast Minimum Uncertainty Search • Minimum uncertainty path between A to B in a graph. • Exhaustive search. 17

  28. FaMUS: Fast Minimum Uncertainty Search • Minimum uncertainty path between A to B in a graph. • Exhaustive search. 17

  29. FaMUS: Fast Minimum Uncertainty Search • Experiment: Are the minimum uncertainty path and the shortest path necessarily equal? • Select two points A and B, and compare the final uncertainty. • 1000 times x 4 datasets. (Biccoca, Intel , New colleges and Manhattan). 24

  30. FaMUS: Fast Minimum Uncertainty Search • Examples of paths. 25

  31. FaMUS: Fast Minimum Uncertainty Search • Summary of results • Improvement of a least 50% in timing respect to the state of the art. [Valencia2011] 26

  32. Whathave I done? Topological 27

  33. Topological • Guiding question: • Where should I go in order to improve my topological map? • Challenges: well-posed and egocentricimages. • Execute a SLAM algorithm (e.g. EKF-SLAM, iSAM). • Compute in each step: A-opt, E-opt , D-opt, Determinant, entropy and mutual Information. 28

  34. Topological • One solution: • Textons (a.k.a gist)- Undelaying Structure- Probabilistic decision 29

  35. Whathave I done? TBD 30

  36. TBD • Which are the confidence intervals in the active predictions? • When do I stop the active behaviour? • Find a relationship between uncertainty and metrical error. • Use other constraints other than uncertainty. • Speed up the decision process. • Real experiments. 31

  37. Active SLAM : a FrameworkMy, on-going, PhD Research Thanks!!! hcarri@unizar.es http://webdiis.unizar.es/~hcarri 32

  38. Experimentos • Primer experimento : acerca del cálculo • Segundo experimento : SLAM activo • Robot simulado ambiente interior : MRPT / C++ • Robot real ambiente interior : Pioneer 3 DX - Ad-hoc • Robot real ambiente interior : DLR dataset • Robot real ambiente exterior : Victoria Park dataset • Robot simulado con horizonte unitario : MRPT / C++ 7

  39. 1E-Robot en ambiente exterior @ VP (I) Escenario: • Área de 350 x 350 m • iSAM • Sensor: Odometría + Laser • 150 landmarks– DA conocida. 13

  40. 1E-Robot en ambiente exterior @ VP (II) – Resultados cualitativos (a)-(f) A-opt, E-opt, D-opt, determinante, entropía y MI. 14

  41. 1E-Robot en ambiente interior ad-hoc (I) Escenario: • Área 6x4 m • 2D EKF-SLAM • Sensor: Odometría + Kinect • 5 landmarks– DA conocida 15

  42. 1E-Robot en ambiente interior ad-hoc (II) – Resultados cualitativos (a)-(f) A-opt, E-opt, D-opt, determinante, entropía y MI. 16

  43. 2E - Análisis cuantitativo 20x20 m • Evolución del MSE ((a)-(c)) y chi2 ((d)-(f)). Promedio de 10 MC. 18

  44. Determinante Operación algebraica que transforma una matriz en un escalar. • Propiedades (matriz n x n) • Geométrica: Volumen del paralelepípedo definido en el espacio n-dimensional. • Homogéneo de grado n. Si, 15

  45. Artículos • “Experimental Comparison of Optimum Criteria for Active SLAM”. Oral presentation in the “III Workshop de Robótica: Robótica Experimental (ROBOT’11)”. • “On the Comparison of Uncertainty Criteria for Active SLAM”. Submitted to ICRA’12. • “Planning Minimum Uncertainty Paths Over Pose/Feature Graphs Constructed Via SLAM” . Submitted to ICRA’12. 18

  46. OntheComparisonof UncertaintyCriteriafor Active SLAM Thanks!!! hcarri@unizar.es http://webdiis.unizar.es/~hcarri 19

  47. FaMUS: Fast Minimum Uncertainty Search 17

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