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Fast Articial Landmark Detection for Indoor Mobile
Robots
Dmitriy Kartashov, Artur Huletski, Kirill Krinkin
The Academic University, SPbETU
2015
Articial landmarks 2015 1 / 14
Introduction
SLAM  simultaneous localization and mapping  is the computational
problem of constructing a map of an unknown environment while
simultaneously keeping track of a robot position within it.
Some SLAM methods use camera as the main source of information about
the environment.
most of such methods are based on extraction of unique environment
features from camera images;
in some cases articial landmarks may be used to simplify environment
markup.
In fact, landmarks may be integrated in any standard SLAM algorithm to
assist a robot in navigation and localization or to give some additional
information about the environment.
Articial landmarks 2015 2 / 14
Landmarks
Landmarks are passive objects in the environment that provide a high
degree of localization accuracy when they are within the robot's eld of
view.
There are many types of landmarks, but printed landmarks have several
advantages comparing to other technologies:
don't consume power;
require only camera;
cheap and easy to produce;
Articial landmarks 2015 3 / 14
Visual landmarks
One possible option for visual landmark is QR code, but this approach has
several problems:
it's rather dicult to detect and read QR code when it is far enough;
QR code detection quality is very sensitive to the angle between
camera and QR code plane.
Another option is geometric and/or color pattern:
exisiting landmarks have diameter equal to 20 cm and can be reliably
detected from 2 meters if horizontal angle lies in range [-60; 60].
Aim
We want to develop a landmark that can be detected in broad horizontal
angle range and has relatively small size at the same time.
Articial landmarks 2015 4 / 14
Landmark design
QR code layout extended with
color markup:
3 blue squares  nder
patterns (FIP);
1 red square  alignment
pattern (AP);
white background
QR code in center is the source
of information;
Articial landmarks 2015 5 / 14
Design explanation
Landmark looks like 4 light squares on a dark background in the
saturation channel of the HSV color space in daylight;
This allows to run some edge detection algorithm, e.g. Canny edge
detector, on the saturation channel to nd contours in the image;
(a) Saturation channel (b) Edge detector output
Articial landmarks 2015 6 / 14
Detection algorithm pipeline
Articial landmarks 2015 7 / 14
Filter stages
Landmark detection is based only on constraints on relative position of
landmark components and not on geometric shape constraints.
1. Edge detector is applied to the saturation channel of the input image.
2. Output image is searched for closed contours.
3. Found contours are checked if they meet the following conditions in
the RGB color space:
blue  a · red and blue  b · green
4. Appropiate contours are pushed into the list of FIP or AP candidates.
Articial landmarks 2015 8 / 14
Geometric stages
1. The FIP graph is constructed in the following way: vertices are FIPs
and an edge connects FIPs if they satisfy two types of constraints:
minimum and maximum distance between FIPs;
dierence in height and width.
2. The resulting graph is searched for 3-cycles.
3. For the FIP triplet AP candidate are chosen if it meets the constraints
on the location and has closest to the selected FIPs size.
4. Resulting quadruple is the landmark candidate.
Articial landmarks 2015 9 / 14
QR code extraction
Given the location of FIPs and APs the perspective transformation can
be computed in order to get orthogonal projection of the landmark.
A QR code located in the center of the landmark can be extracted
using that orthogonal projection and decoded.
(c) Landmark (d) Extracted QR code
Articial landmarks 2015 10 / 14
Optimization
The detection algorithm is supposed to work on mobile robots.
on Raspberry Pi CPU (700 MHz) the algorithm can process only
1-2 images per second;
most computationally expensive steps are lter stages (denoising, HSV
conversion, edge detection).
GPU can be used to improve performance:
these steps are ported to OpenGL ES 2.0 shader language;
with such optimization the algorithm can process up to 10 FPS.
The algorithm code can be found at:
https://github.com/OSLL/landmark-detection
Articial landmarks 2015 11 / 14
Evaluation
Performance
the detection algorithm can process 1 MP images in real time on 2.5
GHz CPU;
with GPU optimizations  2 MP images in real time;
on Raspberry Pi CPU the algorithm processes 1-2 images per second;
with GPU optimizations  up to 10 images per second.
Detection
The landmark with 9 cm side can be reliably detected in 120 degree
horizontal angle range from 2 meters.
The landmark with 15 cm side can be reliably detected in 150 degree
horizontal angle range from 3 meters.
Articial landmarks 2015 12 / 14
Conclusion
Advantages of the designed landmark:
easy to create, setup, detect and identify;
the algorithm performace is sucient to work on small mobile robots.
Disadvantages:
image noise has an impact on detection quality;
the algorithm relies on color features that depend on external lighting.
Possible improvements:
adaptive lters that can adjust their parameters depending on
environment parameters;
video stream can be used for image stabilization, noise reduction and
landmark tracking.
Articial landmarks 2015 13 / 14
Thank You
Articial landmarks 2015 14 / 14

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Fast Artificial Landmark Detection for indoor mobile robots AIMAVIG'2015

  • 1. Fast Articial Landmark Detection for Indoor Mobile Robots Dmitriy Kartashov, Artur Huletski, Kirill Krinkin The Academic University, SPbETU 2015 Articial landmarks 2015 1 / 14
  • 2. Introduction SLAM simultaneous localization and mapping is the computational problem of constructing a map of an unknown environment while simultaneously keeping track of a robot position within it. Some SLAM methods use camera as the main source of information about the environment. most of such methods are based on extraction of unique environment features from camera images; in some cases articial landmarks may be used to simplify environment markup. In fact, landmarks may be integrated in any standard SLAM algorithm to assist a robot in navigation and localization or to give some additional information about the environment. Articial landmarks 2015 2 / 14
  • 3. Landmarks Landmarks are passive objects in the environment that provide a high degree of localization accuracy when they are within the robot's eld of view. There are many types of landmarks, but printed landmarks have several advantages comparing to other technologies: don't consume power; require only camera; cheap and easy to produce; Articial landmarks 2015 3 / 14
  • 4. Visual landmarks One possible option for visual landmark is QR code, but this approach has several problems: it's rather dicult to detect and read QR code when it is far enough; QR code detection quality is very sensitive to the angle between camera and QR code plane. Another option is geometric and/or color pattern: exisiting landmarks have diameter equal to 20 cm and can be reliably detected from 2 meters if horizontal angle lies in range [-60; 60]. Aim We want to develop a landmark that can be detected in broad horizontal angle range and has relatively small size at the same time. Articial landmarks 2015 4 / 14
  • 5. Landmark design QR code layout extended with color markup: 3 blue squares nder patterns (FIP); 1 red square alignment pattern (AP); white background QR code in center is the source of information; Articial landmarks 2015 5 / 14
  • 6. Design explanation Landmark looks like 4 light squares on a dark background in the saturation channel of the HSV color space in daylight; This allows to run some edge detection algorithm, e.g. Canny edge detector, on the saturation channel to nd contours in the image; (a) Saturation channel (b) Edge detector output Articial landmarks 2015 6 / 14
  • 8. Filter stages Landmark detection is based only on constraints on relative position of landmark components and not on geometric shape constraints. 1. Edge detector is applied to the saturation channel of the input image. 2. Output image is searched for closed contours. 3. Found contours are checked if they meet the following conditions in the RGB color space: blue a · red and blue b · green 4. Appropiate contours are pushed into the list of FIP or AP candidates. Articial landmarks 2015 8 / 14
  • 9. Geometric stages 1. The FIP graph is constructed in the following way: vertices are FIPs and an edge connects FIPs if they satisfy two types of constraints: minimum and maximum distance between FIPs; dierence in height and width. 2. The resulting graph is searched for 3-cycles. 3. For the FIP triplet AP candidate are chosen if it meets the constraints on the location and has closest to the selected FIPs size. 4. Resulting quadruple is the landmark candidate. Articial landmarks 2015 9 / 14
  • 10. QR code extraction Given the location of FIPs and APs the perspective transformation can be computed in order to get orthogonal projection of the landmark. A QR code located in the center of the landmark can be extracted using that orthogonal projection and decoded. (c) Landmark (d) Extracted QR code Articial landmarks 2015 10 / 14
  • 11. Optimization The detection algorithm is supposed to work on mobile robots. on Raspberry Pi CPU (700 MHz) the algorithm can process only 1-2 images per second; most computationally expensive steps are lter stages (denoising, HSV conversion, edge detection). GPU can be used to improve performance: these steps are ported to OpenGL ES 2.0 shader language; with such optimization the algorithm can process up to 10 FPS. The algorithm code can be found at: https://github.com/OSLL/landmark-detection Articial landmarks 2015 11 / 14
  • 12. Evaluation Performance the detection algorithm can process 1 MP images in real time on 2.5 GHz CPU; with GPU optimizations 2 MP images in real time; on Raspberry Pi CPU the algorithm processes 1-2 images per second; with GPU optimizations up to 10 images per second. Detection The landmark with 9 cm side can be reliably detected in 120 degree horizontal angle range from 2 meters. The landmark with 15 cm side can be reliably detected in 150 degree horizontal angle range from 3 meters. Articial landmarks 2015 12 / 14
  • 13. Conclusion Advantages of the designed landmark: easy to create, setup, detect and identify; the algorithm performace is sucient to work on small mobile robots. Disadvantages: image noise has an impact on detection quality; the algorithm relies on color features that depend on external lighting. Possible improvements: adaptive lters that can adjust their parameters depending on environment parameters; video stream can be used for image stabilization, noise reduction and landmark tracking. Articial landmarks 2015 13 / 14