EECS498: Autonomous Robotics Laboratory

Transcription

1 EECS498: Autonomous Robotics Laboratory Edwin Olson University of Michigan

2 Course Overview Goal: Develop a pragmatic understanding of both theoretical principles and real-world issues, enabling you to design and program robotic systems incorporating sensing, planning, and acting. Course topics: Kinematics Inverse Kinematics Sensors & Sensor Processing Motors & Control Planning State Estimation Embedded Systems

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4 Evaluation Two major labs, each with multiple check points. ArmLab BotLab Midterm bonus Labs 30% Midterms 32% Final Project 32% Quizzes 5% Course Eval 1%

5 Lab/Project Deliverables In addition to short-response lab writeups: ArmLab Create a poster - Abstract, effective visuals BotLab Oral presentations (e.g. power point) Final project Interactive demonstration in Tishman hall

6 Course Policies Collaboration Peer programming, not parallelization No use of outside resources Teams can share ideas, but not solutions/code Group work certifications I participated and contributed to team discussions on each problem, and I attest to the integrity of each solution. Our team met as a group on [DATE(s)]. Note any qualifications (we re reasonable). Signatures

7 Lateness Assignments due at 11:59p; 10% lateness penalty per day; no credit after three days Excused missed exams/quizzes Quizzes: not considered in grading Exams: oral make-up exams Unexcused exams/quizzes: 0.

8 Lab Policies Food restricted Non-sticky beverages at stations Anything else discouraged, but some tolerance for responsible snacking away from workstation. No removal of equipment without advance permission. Notify staff of accidents, broken equipment. Secret door code: XXXXXX

9 Teams ArmLab & BotLab Teams assigned by staff Final project Student-selected teams Peer Evaluations

10 Teaming Working on a team is an engineering problem in itself. At the beginning of each lab, discuss When/where will you meet? What do you expect of each other? What will you do if problems arise?

11 Final Projects Scope Implement a more complicated algorithm Implement a system of multiple algorithms Develop a principled new algorithm Develop a compelling real-world implementation Evaluation 50% Technical merit 25% Interactivity and engagingness of presentation 25% Web exhibit

12 Course Resources lists Subscribe yourself at: mailman/listinfo/eecs498 Wiki

13 Course Resources Apps Peer evaluations Real-time course standing Books There is no textbook.

14 Shared lab space Lab space is shared with 373 Creates some scheduling hazards!

15 Lab Hours M T W R F Labture Labture Ols 2 Ols/Mort 3 Mort

16 Cameras and Image Formation 16

17 World Simplest Camera? The world Film Just hold up a piece of film Do we get an image on the film? For each piece of the film, where do the photons come from?

18 World Simplest Camera? The world Film Just hold up a piece of film Do we get an image on the film? For each piece of the film, where do the photons come from?

19 World Simplest Camera? The world Film Just hold up a piece of film Do we get an image on the film? For each piece of the film, where do the photons come from?

20 World Simplest Camera? The world Film Just hold up a piece of film Do we get an image on the film? For each piece of the film, where do the photons come from?

21 World Simplest Camera? The world Film Just hold up a piece of film Do we get an image on the film? For each piece of the film, where do the photons come from?

22 World Simplest Camera? The world Film Just hold up a piece of film Do we get an image on the film? For each piece of the film, where do the photons come from?

23 Let s add an aperture An aperture blocks all but a small subset of the rays Causes the image to appear in focus!

24 Aperture Size Why not make the aperture super small? A pin-hole lens. Not enough light to register on our film What happens when the aperture is bigger? More rays can fit through--- blurrier image Is there any way of getting a sharp image, but allow more light through? Yes! A lens.

25 Lenses z f A lens collects rays with a particular divergence and refocuses them to a point. But points at the wrong distance won t be refocused exactly. Depth of field: how much of the scene is in focus We re going to ignore this today, however--- we re going to assume a pin-hole model.

26 Perspective Projection f z x x The pinhole creates two similar triangles Allows us to determine x in terms of x

27 Perspective Projection f z x x The pinhole creates two similar triangles Allows us to determine x in terms of x x = -xf/z (why is it negative? we ll assume from here on out that the camera unflips the image.)

28 Perspective Projection f z x x What are the pixel coordinates where the flame appears? x = fx/z + c Measure f in pixels and add an offset (so that the middle pixel is in the middle of the image)

29 Lens distortions Unfortunately, real (imperfect) lenses further complicate life. Undistorted Pin cushion Barrel (common)

30 Calibration Often use a planar target Compute geometrical relationship between points on (known) target and observed points. For planar targets: a homography Optimize camera parameters to match observed images.

31 Correcting for lens distortion Radial Distortion 1. Compute the pixel coordinates assuming the lens is undistorted 2. Convert to polar form 3. Compute r = f(r) 4. Convert r and θ back to Cartesian coordinates. r,θ Function f() is typically nasty polynomial functions. We find the parameters by using non-linear optimization algorithms

32 Color Cameras Incoming light is described in terms of a power spectral density Color isn t a physical property of light It s made up by our eyes and brain! Different types of incoming light can have the same color S Response M Response L Response Eye

33 Just for fun...

34 Bayer Patterns

35 Bayer Patterns Why does this matter? At each pixel, two color channels are interpolated based on nearby pixels Thus, a color camera is more blurry than a monochrome camera.

36 Bayer Pattern Artifacts When the color of an area is uniform, Bayer patterns work well. What happens when there is a rapid change in color? R, G, and B sub-pixels may observe different PSDs Interpolated colors may not exist anywhere! Average of nearby red pixels = red... so there will be a red output pixel even though the incoming light is either white or black.

37 JCam

38 Visualization Fraction of brain devoted to vision 25-50% (depending on who you ask) That s an awful lot of processing power Try to use it when you re working on a hard problem!

39 Why make visualizations? Visualization is the single best use of researcher time. Find bugs faster Verify algorithms and build intuition Generate figures/ movies for papers/talks

40 Visualization Tips Start by visualizing When designing a system, design your debugging interface first. Visualize creatively Experiment with different rendering schemes. A pretty interface is often a good interface. Exploit time Make movies, not just images Especially with iterative algorithms! Become an expert in a visualization package Vis

41 Example: ICP

42 Minard s Graph of Napoleon s Army

43 Name Voyager

44 Graph Clustering

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46 Vis