CSCI 445 Amin Atrash. Ultrasound, Laser and Vision Sensors. Introduction to Robotics L. Itti & M. J. Mataric

Transcription

1 Introduction to Robotics CSCI 445 Amin Atrash Ultrasound, Laser and Vision Sensors

2 Today s Lecture Outline Ultrasound (sonar) Laser range-finders (ladar, not lidar) Vision Stereo vision

3 Ultrasound/Sonar Ultrasound = sonar Ultrasound comes from: ultra = (far beyond in Latin) + sound Sonar comes from: so(und) + na(vigation and)+ r(anging)

4 Ultrasound: Time of Flight Ultrasound (sonar) range sensing is based on the time-of-flight principle The emitter produces a "chirp" of sound (at a very high frequency) Sound travels away from the emitter, bounces off object(s), eventually returns to the detector The elapsed time is measured Why use high frequency sound?

5 Measuring Distance How do we get distance from the measured time-of-flight? Sound travels at a constant speed, which varies slightly based on ambient temperature At room temperature, sound travels at 331 m/sec, or around 30 cm/msec d = v*t/2

6 Sonar in Biology The process of finding one's location based on sonar is called echolocation ( echo-finding ) Inspiration for ultrasound sensing comes from nature Sound travels well in water; dolphins and whales use it to communicate over long distances Bats are best known for using sonar instead of vision (why do they use it?)

7 Bat Sonar Bats use sonar because they live in very dark caves where vision would be useless Bat sonar is extremely complex compared to synthetic sonar sensors Bats use numerous frequencies for: finding tiny fast-flying prey avoiding cave walls and other bats finding mates communicating with other bats

8 Sonar Sensor Construction Some sensors have a single emitter/detector pair Others have a shared emitter/detector Pros/cons of each?

9 Angular Resolution Typical sonar sensors have 30-degree angular resolution E.g., Polaroid transducer: Frequency 50kHz Wavelength 7mm Sensor diameter ~ 40mm Max range ~ 10m

10 Surface Properties Ultrasonic range sensors depend on reflections from objects in the environment Surface properties are important: Specular vs. diffuse reflection Angle of incidence

11 Specularity v. Diffusion Surfaces generate two forms of reflection: Specular: angle of incidence = angle of reflection Diffuse: energy absorbed and re-emitted at a broad range of angles Specular reflections are strong, but unlikely to return to detector Diffuse reflections are weak, but likely to return to detector

12 Angle of incidence Increasing angle of incidence decreasing strength of reflection I.e., grazing rays generate no detectable return Objects may appear invisible Objects may act as mirrors (multiple specular reflections)

13 Overcoming Sonar Limitations Increase diffuse reflections: Add texture to objects (with wavelength similar to sonar) Use clever post-processing: Build maps with sonar readings from multiple viewpoints Use more advanced sensors: Phased-array emitter/detectors Higher frequencies

14 Uses of Sonar Sensors In spite of specular reflection, ultrasound/sonar sensors are used very successfully Robotics applications: obstacle avoidance mapping Note: sonars do require high power (current) => bigger capacitors and batteries

15 Laser Range Finders (ladar) Sonar sensors measure range using time-of-flight of sound Sound: 330 m/sec in air Laser range finders measure time-offlight of light Light: 300,000,000 m/sec in vacuum Ladar = l(ight r)adar Infrared lasers (invisible to human eye)

16 Ladar vs. Sonar Pros: Small spot size => high good angular resolution High speed => high sampling rate Short wavelength => fewer specular reflections Cons: Small spot size => less coverage Large/heavy Complex/expensive

17 Scanning Ladars Scanning laser rangefinder: Laser range finder Rotating mirror(s) E.g., SICK 3D planar scan, 180 degree FOV E.g., Riegl 3D volume scan, 360/80 degree FOV

18 3D Laser Mapping

19 Machine Vision Cameras (try to) model biological eyes Machine vision systems are complex Machine vision has historically been a separate branch of Artificial Intelligence Vision computationally expensive: very large portions of the human brain are devoted to it (how much?)

20 HumanVision

21 The Physics of Vision The general principle of a camera: Light, scattered from objects in the environment (the scene) goes through an opening (iris, in the simplest case a pin hole, in the more sophisticated case a lens)... impinges on the retina (in biology) or the image plane (in a camera)... and then it is further processed...

22 Cameras For Machine Vision Cameras for machine vision: Lens/Iris Photosensitive pixel array (CCD, CMOS) Signal processing A/D converters (frame grabber) Historically, cameras had analog output, required frame-grabber in computer Modern cameras have digital output (USB, IEEE1394)

23 Steps in Visual Processing Processing is usually divided into discrete stages: Early, middle, late Low-level, mid-level, high-level Low-level processing: Smoothing Edge detection High-level processing: Scene reconstruction Object recognition

24 Low-level Processing Smoothing: Images have noise (pixel variance) Replace pixel values with local averages E.g., mean filter or median filter Edge detection: Strong intensity changes often correspond to object edges Replace pixel values with local gradient E.g., Canny edge detector

25 Mid-level Processing Segmentation: Divide image into components (e.g., foreground/background) Contours (connected edges) Regions (constant color or texture)

26 High-level Processing Scene reconstruction: What world produced this image? Underdetermined problem (many worlds will produce the same image) Object recognition: Given model of object, attempt to find object in the image Match invariant features

27 Use color Practical Tips Use a smaller image plane Use other sensors to complement vision (IR, sonar, contact, etc.) Use task-specific information (e.g., for driving, look for white lines on roads)

28 Stereo Vision Stereo vision (visual ranging): use two cameras with known spatial offset Each camera records slightly different image disparity Infer range of objects that appear in both images from disparity triangulation Outputs: Disparity map: per-pixel disparity Range image: per-pixel range

29 Epipolar Geometry The epipolar plane is the plane defined by a 3D point and the optical centers C and C'. The epipolar line (xy,x y ) is the straight line of intersection of the epipolar plane with the image plane. All epipolar lines intersect at the epipole. The epipole is the image, in one camera, of the image center of the other camera.

30 Stereo Vision Distance from triangulation Correlation problem z = f T d Disparity d = x x Rectification aligns epipolar lines & reduces search to 1D Need internal and external camera parameters - calibration is required

31 Stereo Correspondence?

32 Disparity Map

33 Stereo Vision vs. Ladar Pros of stereo vision: Dense 3D range data High sampling rate Cons of stereo vision: Requires textured surfaces Incomplete range maps Limited range and depth-of-field

34 Textbook Readings MM: Chapter 9