Localization; review GPS and sonar sensors

1. Robotics: Localization; review GPS and sonar sensors نام استاد: دكتر سعيد شيري نام ارائه دهنده: وحيد حقيقت دوست Amirkabir University of Technology Computer Engineering & Information Technology Department

3. Localization,GPS and sonar Robot Motion Planning Under Unertainty x Robocup Soni robots wheeled mobile robot y Motion planning Rescue Rescue Rescue Previous presentations

4. Contents • Localization • Sonar sensors • GPS

5. Mobile Robot Localization • Where am I? • Given a map, determine the robot’s location • Landmark locations are known, but the robot’s position is not • From sensor readings, the robot must be able to infer its most likely position on the field • Example : where are the AIBOs on the soccer field?

6. Mobile Robot Mapping • What does the world look like? • Robot is unaware of its environment • The robot must explore the world and determine its structure • Most often, this is combined with localization • Robot must update its location according to the landmarks • Known in the literature as Simultaneous Localization and Mapping, or Concurrent Localization and Mapping : SLAM (CLM) • Example : AIBOs are placed in an unknown environment and must learn the locations of the landmarks (An interesting project idea?)

7. Initial state detects nothing: Moves and detects landmark: Moves and detects nothing: Moves and detects landmark: Localization

8. What we know…What we don’t know… • We know what the control inputs of our process are • We know what we’ve told the system to do and have a model for what the expected output should be if everything works right • We don’t know what the noise in the system truly is • We can only estimate what the noise might be and try to put some sort of upper bound on it • When estimating the state of a system, we try to find a set of values that comes as close to the truth as possible • There will always be some mismatch between our estimate of the system and the true state of the system itself. We just try to figure out how much mismatch there is and try to get the best estimate possible

9. Problem One: Localization • Given: • World map • Robot’s initial pose • Sensor updates • Find: • Robot’s pose as it moves

10. Localization Foundation • At every time step t: • UPDATEeach sample’s new location based on movement • RESAMPLE the pose distribution based on sensor readings

11. Problem Two: Mapping • Given: • Robot • Sensors • Find: • Map of the environment (and implicitly, the robot’s location as it moves)

12. Simultaneous LocalizationAnd Mapping (SLAM) If we have a map: We can localize! If we can localize: We can make a map!

13. Circular Error Problem If we have a map: We can localize! NOT THAT SIMPLE! If we can localize: We can make a map!

14. How do we solve SLAM? • Incorporate location/map uncertainties into a single model • Optimize robot’s exploratory path • Use geometry (especially indoors)

15. The Localization Problem • Ingemar Cox (1991): “Using sensory information to locate the robot in its environment is the most fundamental problem to provide a mobile robot with autonomous capabilities.” • Position tracking (bounded uncertainty) • Global localization (unbounded uncertainty) • Kidnapping (recovery from failure)

16. Localization’s Sidekick: Globalization • Localization without knowledge of start location • One step further: “kidnapped robot problem”

17. Placed into an environment with a map, not knowing its initial position. “Wake up” – turn on the power How can the robot find its location? Recover from a localization failure. Kidnapped Robot

18. Introduction • Localization: Given sensory input, the agent must be able to infer its position and orientation relative to the global map. Knowledge of current position and orientation are crucial to: • Path-fowloing (and hence to navigation) • Correct placement of newly-discovered landmarks in the enviroment (and hence to map-building) • Registeration of multiple fields of view (and hence to exploration).

19. Introduction(continue) • Navigation: Given a map of the enviroment, the agent should be able to • plane a path from its current location to another location within the global map, • and then follow its path, • possibly updating the path if obstacles are found.

20. Introduction(continue) • Map-Bulding: Upon detection of features and landmaks which do not yet appear in the agent’s internal representation of enviroment, the agent should update that representation. • Exploration: Upon discovery of a new object (not yet in world map) • The agent should have strategy wich allow the object to be viewed from multiple points, and by multiple sensing modalities • The agent should be able to register these multiple views to produce a unified description of the object

21. The problem of localization • The process of navigation can be seen as iteration through the loop depicted in figure. Where Am I? Compare to Intended position Execute Under Dead reckoning Obtain Corrective Maneuver

22. The problem of localization (continue) • “Where am I?” phase constitutes localization • There are different types of localization: • Dead-reckoning localization • Beacon-based localization • Feature-based localization • Landmark-based localization

23. Dead-reckoning localization: • base on internal sensing, usually in form of wheel encoders or some internal navigation system • No sensing of the external environment is performrd • Fast and simple • Prone to error accumulation since no feedback is obtained from the environment • it is usually supplemented with some other form of loclization

24. β (x+∆x, y+∆y, β) α (x, y, α) Dead-Reckoning Accumulated error can be quite big for a period time. (0,0) Dead-Reckoning

25. A robot’s path as obtained by its odometry, relative to a given map. Dead-Reckoning [ S. Thrun, Robotic Mapping: A Survey ]

27. Major Issues with Autonomy • Movement • Inaccuracy • Sensor Inaccuracy • Environmental Uncertainty

28. Beacon-based localization: • Relies on the prior deployment of easily detectable, recognizable and distinguishable beacons in known locations in the environment • The identity of passive beacons must be established by the sensors of mobile agent • Location of the beacons is then accessed from a global map • Active beacons may transmit their identity and current location => less sensing and signal processing on the part of the agent • Global Position System (GPS) is an example of active beacon system

29. Feature-based localization: • Does not rely on modification of the environment. • Instead, “naturally” occurring features are extracted from the data flowing into the sensors on the agent. • Correspondence must then be established between the detected feature and the features stored in the map. • Correspondence is then established by finding a transformation which brings the local coordinate frame of the agent into the global coordinate frame of the map

30. Feature-based localization: (continue) • This process is confound by the “phantom” features, failure to detect some features, uncertainty and noise… • The correspondence problem is the chief reason why feature-based localization is much more difficult than beacon-based localization

31. landmark-based localization: • Relies on the detection of uniquely identifiable features. • The correspondence problem is alleviated since the set of possible matches for each detected landmark is very small. • The detection and identification of a landmark generally requires significantly more intensive signal processing and higher-level reasoning than feature-detection. • The onus is shifted from establishing correspondence to recognition of landmarks

32. Update period • For a system involving a combination of dead reckoning with some other localization, the accumulation of position and orientation error follows the pattern delineated in figure. The goal of the localization system is to keep the accumulated error within tolerable limits.

33. Update period depends on… • The sensor modalities selected • The types and densities of beacons, features or landmarks in the environment • The computational complexity of the algorithms used for beacon, feature or landmark detection

34. Continuous localization verse relocation • One of the most computationally intensive aspect of feature based localization is correspondence matching. • It is possible to compare actual against predicted measurements which are deemed to match, no further correspondence need be established; previous correspondences have been preserved. If sufficient matches are found to allow position and orientation estimation, localization is greatly facilitated. This method calls continuous localization.

35. Continuous localization verse relocation (continue) • When an insufficient number of matches between actual and predicted measurements occurs, the agent is considered to be “lost”. In this case, correspondence between extracted and map features must be re-established. Such a process is more computationally intensive, and is named relocation.

36. Continuous localization verse relocation (continue) • The incorporation of continuous localization allows the basic navigatin loop modified. Where Am I? Compare to Intended position Execute Under Dead reckoning Obtain Corrective Maneuver

37. Where Am I? Relocation NO Correspondence Preserved? YES Continuous localization Execute Under Dead reckoning Compare to Intended position Obtain Corrective Maneuver

38. The central role of localization • Localization competency is central not only for navigation, but also for exploration and map-building by a mobile agent. • If we don’t have reliable localization: • Registeration of multiple views of the same object become more difficult • Feature landmark and obstacle discovered during an exploratory phase cannot be placed in their correct positions in a map • No feedback loops can be implemented for path-folloewing during navigation.

39. exploration navigation Localization map-building

40. Navigation algorithms • Approaches to obstacle avoidance, such as the method of potential fields, are typically of no use for localization. • Algorithms for globally referenced position estimation, rely on priori map, but do not address the construction of such a map • Many algorithms for map building don't address the issue of localization while the map is being constructed, relying instead on odometry or hand-measuring of sensing locations.

41. Sensing modalities: Three modalities: • Ultrasound • Patterned-light • Stereo modalities Have been used extensively over the past three decades

42. Ultrasound

43. Ultrasound • Introduction • Physics of sonars • What can be inferred from a single sonar? • What can be expected from multiple measurements? • Clustering and parameter estimation • Specification of an algorithm

44. Ultrasonic Sonar Sensor

45. Ultrasound Physics of sonars • The large difference in the acoustic impedance between most solid surfaces appear as acoustic reflectors. • Further, the generally large wavelengths of ultrasonic energy emitted in air ensure that most reflections are specular. • We assume at the lowest level that each sonar measurement is generated by an element of the set of basic features P={planar reflective patches, outer diffractive corners, inner reflective corners}

46. Ultrasound What can be inferred from a single sonar? • All the one can infer from a single measurment is the existence of an element of P at the distance r somewhere along the boundary of the transmitted cone truncated at range r. P={planar reflective patches, outer diffractive corners, inner reflective corners}

47. Ultrasound What can be inferred from a single sonar? • In the case of planar reflective patches, the patch has orientation tangential to the acoustic wave fronts.

48. Ultrasound What can be inferred from a single sonar? • In 3D the region of the location of the reflective patch forms a section of the surface of a sphere, centered at the transducer and of solid angle 2п(1-cos α) steradians, where α is the half-width of ultrasonic emission cone. α ≈ 15º => surface of area = 2пr²(1-cos α) ≈ 0.214r²

50. Ultrasound What can be expected from multiple measurements? • For multiple measurements generated by the same planar surface, all arcs, in the noise free to the case, share a common tangent; corners (both inner reflective and outer diffractive) induce measurements whose arcs intersect at the corner. In general a smooth curve defined parametrically by piecewise differentiable, will induce measurements such that the arc corresponding to each measurements intersects the curve at a point where both curve and arc share the tangent of orientation at that point.