16311: Introduction to Robotics Lab 2 – Robot Vision January 22, 2014 Alimpon Sinha

1 / 11

# 16311: Introduction to Robotics Lab 2 – Robot Vision January 22, 2014 Alimpon Sinha - PowerPoint PPT Presentation

16311: Introduction to Robotics Lab 2 – Robot Vision January 22, 2014 Alimpon Sinha (shamelessly using old slides). 16-311 Introduction to Robotics – Lab 2, p. 1. Thresholding. Making a binary, black-and-white image. Given an image and a threshold value,

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about ' 16311: Introduction to Robotics Lab 2 – Robot Vision January 22, 2014 Alimpon Sinha' - glora

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

16311: Introduction to Robotics

Lab 2 – Robot Vision

January 22, 2014

Alimpon Sinha

(shamelessly using old slides)

16-311 Introduction to Robotics – Lab 2, p.1

Thresholding

Making a binary, black-and-white image. Given an image and a threshold value,

• Set all pixels of value ≤ threshold to 0
• Set all pixels of value > threshold to 255 (or just 1)

0 128 255

16-311 Introduction to Robotics – Lab 2, p.2

Thresholding

threshold(image, 75);

Every pixel with value lower than 75: set to 0

Every pixel with value higher than 75: set to 255

0 128 255

75

16-311 Introduction to Robotics – Lab 2, p.3

Thresholding

Good for removing noise and artifacts.

Of course, overdoing it would lose you precious details.

0

255

0 128 255

16-311 Introduction to Robotics – Lab 2, p.4

Segmentation and Stereo

Segmentation: A program that segments a binary image into connected components

Algorithm – Double Raster Scan in Notes

Stereo - Way of calculating depth from two dimensional images using two cameras

What you’re doing

Building a vision pipeline to estimate the distance to two targets

Targets have tennis ball ‘fiducials’ in known square pattern

- Can be triangulated with ‘stereo’

Steps:

- Thresholding

- Segmentation

- Filtering / Data Association

- Triangulation

16-311 Introduction to Robotics – Lab 2, p.6

Example

1

2

4.2 feet!

4

3

16-311 Introduction to Robotics – Lab 2, p.7

• How do you pick your thresholds?
• How do you identify the fiducial segments? What features might be useful for this?
• What are sources of inaccuracy? How do we account for them?

16-311 Introduction to Robotics – Lab 2, p.8

• Programming (35 points)
• Threshold stage captures ball pixels - 5 points
• Segmentation stage groups ball pixels - 10 points
• Filtering/data association stage identifies ball segments correctly - 10 points
• Triangulation stage estimates target distance to within 20% - 10 points
• Additional points may be awarded for creative/impressive pipelines
• Evaluation (10 points)
• Take photos from prescribed distances - 5 points
• Data collection procedure explained clearly - 5 points
• Analysis (20 points)
• Identify and explain potential sources of error - 10 points
• Support analysis with experiments - 10 points

16-311 Introduction to Robotics – Lab 2, p.9

Important stuff

Due date

Tuesday, January 28th

Handin

Handin a zip file of your submission on blackboard.

Make sure the zip file has your source code, images, and website.

Make sure your images for all pipeline stages are labeled clearly!

16-311 Introduction to Robotics – Lab 2, p.10

Need help?

Email

TA Office Hours

• Have some scheduled for next 7 days.
• In the REL
• See website for the schedule

16-311 Introduction to Robotics – Lab 2, p.11