# Direct Methods for Aibo Localization - PowerPoint PPT Presentation

Direct Methods for Aibo Localization

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Direct Methods for Aibo Localization

## Direct Methods for Aibo Localization

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##### Presentation Transcript

1. r2 r1 y x Direct Methods for Aibo Localization CSE398/498 04 March 05

2. Administration • Plans for the remainder of the semester • Localization Challenge • Kicking Challenge • Goalie Challenge • Scrimmages • Finish CMU Review • Direct methods for robot localization • Mid-semester feedback questionnaire

3. CMU Review (cont’d)

4. References • “CMPack-02: CMU’s Legged Robot Soccer Team,” M. Veloso et al • “Visual Sonar: Fast Obstacle Avoidance Using Monocular Vision,” S. Lenser and M. Veloso, IROS 2003, Las Vegas, USA

5. Visual Sonar • Based entirely upon color segmentation • The main idea: • There are only a handful of colors on the field • Each color can be associated with one or more objects • green -> field • orange -> ball • white -> robot or line • red or blue -> robot • cyan or yellow -> goal http://www-2.cs.cmu.edu/~coral-downloads/legged/papers/cmpack_2002_teamdesc.pdf

6. Visual Sonar (cont’d) • Based entirely upon color segmentation • The main idea: • Discretize the image by azimuth angle • Search in the image from low elevation angle to high for each azimuth angle • When you hit an interesting color (something not green), evaluate it • You can infer the distance to an object for a given azimuth angle from the elevation angle and the robot geometry http://www-2.cs.cmu.edu/~coral-downloads/legged/papers/cmpack_2002_teamdesc.pdf

7. Visual Sonar (cont’d) • By panning head you can generate a 180o+ range map of the field • Subtleties: • Identifying tape • Identifying other robots??? • Advantages over IRs • Video Link http://www-2.cs.cmu.edu/~coral-downloads/legged/papers/cmpack_2002_teamdesc.pdf

8. A Similar Approach… • Based entirely upon edge segmentation • The main idea: • All edges are obstacle • All obstacle must be sitting on the ground • Search in the image from low elevation angle to high for each bearing angle • When you hit an edge, you can infer the distance to an obstacle for a given bearing angle from the elevation angle

9. Why Does this Work? • Recall that edges correspond to large discontinuities in image intensity • While the carpet has significant texture, this pales in comparison with the white lines and green carpet (or white Aibos and green carpet)

10. Why Does this Work? (cont’d) • Let’s look at a lab example • OK, that did not work so great because we still have a lot of spurious edges from the carpet that are NOT obstacles • Q: How can I get rid of these? • A: Treat these edges as noise and filter them. • After applying a 2D gaussian smoothing filter to the image we obtain…

11. Position Updates • Position updates are obtained using the field markers and the goal edges • Both the bearing and the distance are estimated to each field marker. • To estimate the pose analytically, the robot needs to view 2 landmarks simultaneously • CMU uses a probabilistic approach that can merge individual measurement updates over time to estimate the pose of the Aibo • We will discuss this in more detail later in the course

12. + The Main Idea… • Flashback 3 weeks ago • Let’s say instead of having 2 sensors/sensor model, we have a sensing and a motion model • We can combine estimate from our sensors and our motion over time to obtain a very good estimate of our position • One “slight” hiccup…

13. The Kidnapped Robot Problem • If you are going to use such probabilistic approaches you will need to account for this in your sensing/motion model

14. Summary • We reviewed much of the sensing & estimation techniques used by the recent CMU robocup teams • Complete reliance on the vision system – primarily color segmentation • Newer approaches also rely heavily on line segmentation – we may not get to this point • There is a lot of “science” in the process • There are a lot of heuristics in the process. There work well on the Aibo field, but not in a less constrained environment • Approaches are similar to what many other teams are using • Solutions are often not pretty - often the way things are done in the real world

15. Robot Localization

16. References • A. Kelly, Introduction to Mobile Robots Course Notes, “Position Estimation 1-2” • A. Kelly, Introduction to Mobile Robots Course Notes, “Uncertainty 1-3”

17. Robot Localization • The first part of the robot motion planning problem was “Where am I” • Localization refers the ability of the robot to infer its pose (position AND orientation) in the environment from sensor information • We shall examine 2 localization paradigms • Direct (or reactive) • Filter base approaches

18. Direct (or Reactive) Localization • This technique takes the sensor information at each time step, and uses this to directly estimate the robot pose. • Requires an analytical solution from the sensor data • Memoryless • Pros: • Simple implementation • Recovers quickly from large sensor errors/outliers • Cons: • Requires precise sensor measurements to obtain an accurate pose • May require multiple sensors/measurements • Example: GPS

19. High Level Vision: Marker Detection • Marker detection will (probably) be your basis for robot localization as these serve as landmarks for pose estimation • Need to correctly associate pairs of segmented regions with the correct landmarks * www.robocup.org

20. Range-based Position Estimation r1 r2 • One range measurement is insufficient to estimate the robot pose estimate • We know it can be done with three (on the plane)? • Q: Can it be done with two? • A: Yes.

21. r1 r2 y x Range-based Position Estimation (cont’d) • We know the coordinates of the landmarks in our navigation frame (“field” frame) • If the robot can infer the distance to each landmark, we obtain • Expanding these and subtracting the first from the second, we get: which yields 1 equation and 2 unknowns. However, by choosing our coordinate frame appropriately, y2=y1

22. r1 r2 y x Range-based Position Estimation (cont’d) • So we get • From our first equation we have which leaves us 2 solutions for yr. • However by the field geometry we know that yr ≤ y1, from which we obtain

23. y x Range-based Position Estimation (cont’d) • So we get • From our first equation we have which leaves us 2 solutions for yr. • However by the field geometry we know that yr ≤ y1, from which we obtain r1 r2 ISSUE 1: The circle intersection may be degenerate if the range errors are significant or in the vicinity of the line [x2-x1, y2-y1]T

24. r1 r2 y x Range-based Position Estimation (cont’d) • So we get • From our first equation we have which leaves us 2 solutions for yr. • However by the field geometry we know that yr ≤ y1, from which we obtain ISSUE 2: Position error will be a function of the relative position of the dog with respect to the landmark

25. Error Propagation from Fusing Range Measurements (cont’d) • In order to estimate the Aibo position, it was necessary to combine 2 imperfect range measurements • These range errors propagate through non-linear equations to evolve into position errors • Let’s characterize how these range errors map into position errors • First a bit of mathematical review…

26. Error Propagation from Fusing Range Measurements (cont’d) • Recall the Taylor Series is a series expansion of a function f(x) about a point a • Let y represent some state value we are trying to estimate, x is the sensor measurement, and f is a function that maps sensor measurements to state estimates • Now let’s say that the true sensor measurement x is corrupted by some additive noise dx. The resulting Taylor series becomes • Subtracting the two and keeping only the first order terms yields

27. Error Propagation from Fusing Range Measurements (cont’d) • For multivariate functions, the approximation becomes where the nxm matrix J is the Jacobian matrix or the matrix form of the total differential written as: • The determinant of J provides the ratio of n-dimensional volumes in y and x. In other words, |J| represents how much errors in x are amplified when mapped to errors in y

28. Error Propagation from Fusing Range Measurements (cont’d) • Let’s look at our 2D example to see why this is the case • We would like to know how changes in the two ranges r1 and r2 affect our position estimates. The Jacobian for this can be written as • The determinant of this is simply • This is the definition of the vector (or cross) product

29. Error Propagation from Fusing Range Measurements (cont’d) v1 v2 • Flashback to 9th grade… • Recall from the definition of the vector product that the magnitude of the resulting vector is • Which is equivalent to the area of the parallelogram formed from the 2 vectors • This is our “error” or uncertainty volume

30. r1 r2 y x Error Propagation from Fusing Range Measurements (cont’d) • For our example, this means that we can estimate the effects of robot/landmark geometry on position errors by merely calculating the determinant of the J • This is known as the position (or geometric) dilution of precision (PDOP/GDOP) • The effects of PDOP are well studied with respect to GPS systems • Let’s calculate PDOP for our own 2D GPS system. What is the relationship between range errors and position errors?

31. Position Dilution of Precision (PDOP) for 2 Range Sensors The take-home message here is to be careful using range estimates when the vergence angle to the landmarks is small • The blue robot is the true poistion. • The red robot shows the position • estimated using range measurements • corrupted with random Gaussian noise • having a standard deviation equal to • 5% of the true range

32. Error Propagation from Fusing Range Measurements (cont’d) Bad Good QUESTION: Why aren’t the uncertainty regions parallelograms? Bad

33. y x Inferring Orientation • Using range measurements, we can infer the position of the robot without knowledge of its orientation θ • With its position known, we can use this to infer θ • In the camera frame C, the bearings to the landmarks are measured directly as • In the navigation frame N, the bearing angles to the landmarks are • So, the robot orientation is merely NOTE: Estimating θ this way compounds the position error and the bearing error. If you have redundant measurements, use them.

34. x y Bearings-based Position Estimation • There will most likely be less uncertainty in bearing measurements than range measurements (particularly at longer ranges) • Q: Can we directly estimate our position with only two bearing measurements? • A: No.

35. Bearings-based Position Estimation • The points (x1,y1), (x2,y2), (xr,yr) define a circle where the former 2 points define a chord (dashed line) • The robot could be located anywhere on the circle’s lower arc and the inscribed angle subtended by the landmarks would still be |α2- α1| • The orientation of the dog is also free, so you cannot rely upon the absolute bearing measurements. • A third measurement (range or bearing is required to estimate position) NOTE: There are many other techniques/constraints you can use to improve the direct position estimate. These are left to the reader as an exercise.

36. r1 r2 Range-based Position Estimation Revisited • We saw that even small errors in range measurements could result in large position errors • If the noise is zero-mean Gaussian, then averaging range measurements should have the effect of “smoothing” (or canceling) out these errors • Let’s relook our simulation, but use as our position estimate the average of our 3 latest position estimates. In other words…

37. Position Estimation Performance forDirect and 3-element Average Estimates Direct Estimate Mean Filter • The blue robot is the true poistion. • The red robot shows the position • estimated using range measurements • corrupted with random Gaussian noise • having a standard deviation equal to • 5% of the true range

38. Position Error Comparison Position Estimates Errors from 3 element Mean Filter Direct Position Estimate Errors (unfiltered) • Averaging is perhaps the simplest technique for filtering estimates over time • We will discuss this in much greater detail after the break