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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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slide1

Active SLAM : a Framework

My, on-going, PhD Research

Henry Carrillo Lindado

Advised by:

José A. Castellanos

bio academic background
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

so what is my phd about
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

preliminaries slam
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.

preliminaries active slam i
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

preliminaries active slam ii
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

preliminaries active slam ii1
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

preliminaries active slam ii2
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

preliminaries active slam ii3
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

uncertainty criteria for active slam i
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

uncertainty criteria for active slam ii
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

uncertainty criteria for active slam iii
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

first experiment
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

1e simulated robot indoor environment i
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

1e simulated robot indoor environment ii qualitative results
1E-Simulated Robot indoor environment (II) Qualitative results

(a)-(f) A-opt, E-opt, D-opt, determinant, entropy and MI.

12

1e real robot indoor environment @ dlr
1E-Real Robot indoor environment @ DLR

Scenario:

  • Area60x40 m
  • Sensor:

Odometry + Camera

  • 2D EKF-SLAM
  • 576 landmarks –

DA known.

13

1e real robot indoor environment @ dlr qualitative results
1E-Real Robot indoor environment @ DLRQualitative results

(a)-(f) A-opt, E-opt, D-opt, determinant, entropy and MI.

14

first experiment quantitative analysis
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

second experiment
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

2e simulated robot indoor environment i
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

2e simulated robot indoor environment ii
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

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

19

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

20

take home message
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

what have i done1
Whathave I done?

Metrical: an example using D-opt

22

famus fast minimum uncertainty search
FaMUS: Fast Minimum Uncertainty Search
  • Minimum uncertainty path between A to B in a graph.
  • Exhaustive search.

17

famus fast minimum uncertainty search1
FaMUS: Fast Minimum Uncertainty Search
  • Minimum uncertainty path between A to B in a graph.
  • Exhaustive search.

17

famus fast minimum uncertainty search2
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

famus fast minimum uncertainty search4
FaMUS: Fast Minimum Uncertainty Search
  • Summary of results
  • Improvement of a least 50% in timing respect to the state of the art. [Valencia2011]

26

what have i done2
Whathave I done?

Topological

27

topological
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

topological1
Topological
  • One solution:
    • Textons (a.k.a gist)- Undelaying Structure- Probabilistic decision

29

slide36
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

active slam a framework my on going phd research
Active SLAM : a FrameworkMy, on-going, PhD Research

Thanks!!!

hcarri@unizar.es

http://webdiis.unizar.es/~hcarri

32

experimentos
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

1e robot en ambiente exterior @ vp i
1E-Robot en ambiente exterior @ VP (I)

Escenario:

  • Área de 350 x 350 m
  • iSAM
  • Sensor: Odometría + Laser
  • 150 landmarks– DA conocida.

13

1e robot en ambiente exterior @ vp ii resultados cualitativos
1E-Robot en ambiente exterior @ VP (II) – Resultados cualitativos

(a)-(f) A-opt, E-opt, D-opt, determinante, entropía y MI.

14

1e robot en ambiente interior ad hoc i
1E-Robot en ambiente interior ad-hoc (I)

Escenario:

  • Área 6x4 m
  • 2D EKF-SLAM
  • Sensor: Odometría + Kinect
  • 5 landmarks– DA conocida

15

1e robot en ambiente interior ad hoc ii resultados cualitativos
1E-Robot en ambiente interior ad-hoc (II) – Resultados cualitativos

(a)-(f) A-opt, E-opt, D-opt, determinante, entropía y MI.

16

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

18

determinante
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

art culos
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

on the comparison of uncertainty criteria for active slam
OntheComparisonof UncertaintyCriteriafor Active SLAM

Thanks!!!

hcarri@unizar.es

http://webdiis.unizar.es/~hcarri

19