Nowadays the big challenge in simultaneous local- ization and mapping (SLAM) of mobile robots is the creation of efficient and robust algorithms. Significant Number of SLAM algorithms rely on unique features or or use artificial landmarks received from camera images. Feature points and landmarks extraction from images have two significant drawbacks: CPU consumption and weak robustness depending on environment conditions. In this paper we consider performance issues for landmark detection, introduce a new artificial landmark design and fast algorithm for detecting and tracking them in arbitrary images. Also we provide results of performance optimization for different hardware platforms.

1. Fast Articial Landmark Detection for Indoor Mobile
Robots
Dmitriy Kartashov, Artur Huletski, Kirill Krinkin
The Academic University, SPbETU
2015
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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.
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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;
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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.
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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;
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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
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7. Detection algorithm pipeline
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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.
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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.
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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
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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
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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.
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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.
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14. Thank You
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