Date: Fri, 29 Mar 2024 16:52:45 +0100 (CET)
Message-ID: <1492222035.189.1711727565664@cpm>
Subject: Exported From Confluence
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The Indoor Positioning System (IPS) detects the position and ori=
entation (pose) and identity of multiple vehicles simultaneously. The poses=
are updated at 50 Hz.
Optics
The Indoor =
Positioning System Setup works with active light sources (LEDs) on the =
vehicles and a camera looking down from the ceiling. The LEDs are detected =
based on their high brightness. The camera is set to a very short exposure =
(~100 microseconds). Thus, the ambient light creates a very small signal (a=
lmost black), while the LEDs still appear as white dots. The short exposure=
time also eliminates the problem of motion blur. At a top speed of 4 m/s t=
he vehicle travels 0.4 mm during the exposure.
Other light sources and reflective surfaces on the vehicle can create fa=
lse signals and must be covered with tape. This includes the connectors on =
the Raspberry Pi and the status LEDs on the motor speed controller.
=
span>
Vehicle Pose
The outer three LEDs indicate the vehicle pose and are permanently illum=
inated. The section Po=
se Calibration describes how the LED positions are related to the refer=
ence pose.
Vehicle Identifica=
tion
The central LED flashes in a pattern that is different for each vehicle.=
The patterns are chosen such that sampling effects do not create ambiguous=
signals. See our paper Vision-Based Real-Time Indoor Positionin=
g System for Multiple Vehicles for more information.
Vehicle ID |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
16 |
17 |
18 |
19 |
20 |
21 |
22 |
23 |
24 |
25 |
Period of flashing, in number of frames |
4 |
7 |
10 |
13 |
16 |
7 |
10 |
13 |
16 |
19 |
10 |
13 |
16 |
19 |
22 |
13 |
16 |
19 |
22 |
25 |
16 |
19 |
22 |
25 |
28 |
LED illumination duration, in number of frames=
td>
| 2 |
2 |
2 |
2 |
2 |
5 |
5 |
5 |
5 |
5 |
8 |
8 |
8 |
8 |
8 |
11 |
11 |
11 |
11 |
11 |
14 |
14 |
14 |
14 |
14 |
Software Architectu=
re
The IPS software has two major components, the LED detection and the IPS=
pipeline. The LED detection reads images from the camera at 50 Hz and extr=
acts the list of image coordinates for all visible LEDs. These LedPoi=
nts
are published via DDS. The raw images are not saved or published=
, as this would create impractically large data volumes. The IPS pipeline p=
rocesses the LED points and publishes VehicleObserv=
ation
s, which include the vehicle's position, orientation, id=
entity and a timestamp.
=
p>
LED Detection
The LED detection uses the OpenCV functions cv::threshold
, =
cv::findContours
and cv::moments
to find the cent=
ers of the LEDs.
IPS Pipeline
The IPS pipeline performs a relatively complex data processing task. To =
make the software easier to understand, it is broken down into independent,=
stateless processing steps.
=
p>
The UndistortPoints function transforms the image/pixel=
coordinates to metric coordinates on the driving surface (floor). The calc=
ulation is based on a calibration image. See https://github.com/embedd=
ed-software-laboratory/cpm-lab/tree/master/matlab_scripts/camera_calibratio=
n
The DetectVehicles function groups and classifies point=
s as front
, center
, back_left
or back_right. Points that appear to not belong to a vehicle are dis=
carded.
The Queue collects the 50 most recent samples of the ve=
hicle points. This is necessary, as the vehicle ID can not be determined fr=
om a single image.
The DetectVehicleID function extracts the ID for each v=
ehicle from the last second (50 frames) of vehicle points.
The PoseCalculation function calculates the vehicle ref=
erence pose based on the front
, back_left
and back_right points. The calculation is based on manually collected=
calibration data. See https://github.com/embedded-software-laborato=
ry/cpm-lab/tree/master/matlab_scripts/ips_pose_calibration
The implementation can be found at https://github.com/embedded-software-laboratory/cpm-lab/t=
ree/master/ips2/src
Calibration
Camera Calibration
First, other PVC canvases are removed from the floor to reveal the check=
erboard canvas. Then an image of the checkerboard pattern is taken using th=
e PylonViewerApp
. The exposure is adjusted such that the check=
erboard corners are well resolved. If the exposure is too short or long, th=
e corners are "rounded off", which degrades the quality of the calibration.=
The Matlab function detectCheckerboardPoints
is used to det=
ermine a list of matching points (x_floor, y_floor, x_image, y_image)=
. Then a 5th order 2D polynomial is fitted to the data using linear =
least squares.
See https://github.com/embedded-software-laboratory/cpm-lab/tree/=
master/matlab_scripts/camera_calibration
Pose Calibration
The vehicle pose has the format (x, y, yaw)
. The yaw is the=
rotation angle in radians of the vehicle around the vertical axis in the c=
ounter-clockwise direction. When yaw=3D0, the vehicle points in the x-direc=
tion. The coordinates (x, y) give the location of the vehicle's referen=
ce point. The reference point is defined as the geometric center betwe=
en the front and rear axle.
To accurately implement this definition of the pose, another measurement=
and calibration procedure is performed. First we need a means of accuratel=
y placing the vehicle on the floor, such that the true pose is known. The v=
ehicle is clamped into a piece of wood, which extends the vehicle's local x=
/y coordinate system. This makes it simple to manually align the vehicle wi=
th the checkerboard pattern.
While the IPS is running, the vehicle is placed in various poses on the =
floor, following a particular calibration seq=
uence. The calibration features (back_x, back_y, direction_x, dir=
ection_y)
are recorded from the running IPS. A linear calibr=
ation is then fitted using least squares.
Coordinate Systems
IPS Image Coordinate=
s
The image coordinates correspond to the IPS image sensor pixels. They ar=
e only relevant for the IPS LED detection.
Floor Coordinates
(mathjax-inline(x \in [0, 4.5], y \in [0, 4])mathjax-inline)
X/Y units: Meter
Yaw unit: Radian
Origin: corner of the PVC canvas, towards the windows on the right.
Yaw angle: Measured from the x-axis, counterclockwise.
Useful yaw equations:
direction_x =3D cos(yaw)
direction_y =3D sin(yaw)
yaw =3D atan2(=
direction_y, direction_x)
Vehicle Coordinates=
h2>
X-axis: forwards
Y-axis: left
Origin: center point between both axles.
In practice, this coordinate system is realized with a calibration jig.
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