Localization and Approaching to the Human by Mobile

Localization and Approaching to the Human by Mobile
Localization and Approaching to the Human by Mobile Home Robot
Saeed Shiry Ghidary
Computer System, Eng. Faculty,
Kobe University,
Kobe, Nada Ku, 1-1,657
Yasushi Nakata, Toshi Takamori, Motofumi Hattori
Computer System, Eng. Faculty,
Kobe University,
Kobe, Nada Ku, 1-1,657
Abstract
This paper introduces a robotic system that uses a PTZ
camera to find the human in the room and locate its
position. Then the robot is guided to go near to the
human. This includes human detection and localization,
robot localization and robot navigation. Human's head and
face is detected by using a combination of motion
detection, shape analysis and color information. The 3-D
position of human’s head and face is estimated by using
the depth from focus method. For robot localization we
use a very fast measuring system that utilizes ultrasonic
and infrared signals simultaneously. For robot navigation
we use a map of environment consisting of doors, walls
and static objects. After locating human and robot a
visibility graph is searched for shortest path and obstacle
avoidance is done in a reflexive method using ultrasonic
sensors.
Human Det ect i on
HRPS
Robot Pos i t i oni n g
Ski n Col or
I nf r omat i on
US and IR r eci ever s
i n t he Cei l i ng
Mot i on
Det ect i on
Hos t Comput er
Shape Anal ys i s
Act i veCamera
Cont rol
Fuz z y
Cont r ol l er
Human Pos i t i oni ng
1 Introduction
The ability to recognize human inside the room and
localize its 3D position is subject of research in many
different applications. In home robot and human
interaction it would be necessary for robot to know where
is the location of person in the environment.
The purpose of robotic system introduced in this paper is
to find the human in the room and guide the robot to go
near to him or her. This may be used for example in
delivering some thing to the human or other kind of
interaction and support. In this paper we will address
some basic problems for human support by home robot.
This includes human recognition and localization, robot
positioning in indoor environment and robot navigation.
We describe an application where a mobile robot uses a
camera to detect the human and estimate its position.
Dept h
f r om Focus
US and I R t r ans mi t t er
on Robot
Map
Bas ed Navi gat i on
Vi s i bi l i t y Gr aph
Robot
US Obs t acl e
avoi dance
Fig. 1. System overview showing relationship of each
module.
2 System structure
This system is implemented on the Ymabico mobile
robot. Our robot is equipped with a Sony EVI D30 PTZ
CCD camera, which is used for human detection and
localization. There are 8 ultrasonic transceivers around the
robot for obstacle avoidance. The robot communicates
with host computer with wireless LAN. Figure 1 shows
the overview of system and relationship between its
component.
A
B
C
D
E
F
Fig. 2. a) Person working with computer. b) Difference binary image. c) Located head. d) Color detection in whole scene. e)
Color detection in head area. f) Face position relative to image center.
Host computer is a controlling computer used for
global robot localization and path planning. It is
responsible for finding the robot in the room and guiding
it toward the human. There is a master slave relationship
between host computer and mobile robot. Host computer
consists of a powerful PC with digital I/O interface and
wireless LAN connection. It collects data from sensors on
the mobile robot or other sensors in the environment and
after processing the data, orders the mobile robot to do the
necessary function. Commands such as "move", "turn",
"capture" and "measure" are sent by host computer to the
robot and the robot provides different data for the host.
We find the position of the robot in the room using
HRPS [1] system which consists of ultrasonic and
infrared sensors. We have installed ultrasonic and
infrared receivers in the environment for robot
localization.
Robot uses CCD camera to search for the human using
skin color information, motion detection and shape
analysis. Then by active control of camera parameters
robot makes the camera to automatically focus on the face
and uses information from focusing ring of camera to
measure distance between human face and camera. By
having information about the absolute position of robot
and camera and its pan and tilt angles it is possible to find
absolute position of the person in the room.
We use an absolute world model for automatic path
planning and execution. After locating the robot and
human, the whole path of the mobile robot is computed by
searching in the visibility graph. Then the robot is guided
to traverse this path and avoid obstacles in a reactive
method. In the following section details
implementation of each module is explained.
of
3 Human detection and localization
There is a large amount of research in human head and
face detection using motion [2], color information [3],
shape [4] or combination of them [5].
Our system locates a human's head and face by using
motion detection, Hough transform and a statistical color
model. Face color provides a simple and fast tracking cue
for active camera control, which tries to locate face inside
a window by changing pan, tilt and zoom of camera based
on fuzzy rules. This will put the face in the center of the
camera’s filed of view.
By calculating the temporal derivative of an image and
thersholding at a suitable level to filter out noise we
segment the image into regions of motion and stationary
objects (fig. 2.b). We use the difference image from
motion analysis to localize the head. Head can be
supposed to be a nearly rigid object and its shape in an
image can be approximated by a rigid two-dimensional
model. We use a circular model for representing the head.
This model may not completely fit the human head, but it
is simple and provides a fast head locating method. We
search in motion data for the top point of the head and use
a simplified Hough transform to locate circle passing from
this point (fig. 2.c).
Then we search for facial area inside the head circle
(fig. 2.e) using a statistical color model that consists of
two-dimensional Gaussian distribution of normalized face
colors. Although different people have different color
appearances, several studies have shown that such a
difference can be reduced by intensity normalization and
skin colors of different races fall into small cluster in
normalized RGB or HSV color spaces [6].
We trained the statistical classifier from a sampling of
peoples with a variety of races and skin colors. The
parameters of the distribution are obtained by maximum
likelihood estimation. A pixel is identified to have skin
color if corresponding probability is greater than a
threshold. The details of head and face detection are
reported in our previous work [8]. Figure 2 shows steps
taken to detect head and face by proposed method.
4 Robot localization
In order to robot to be useful it must be able to localize
itself in the environment. We use two mechanisms to find
position of the robot; HRPS [1] and dead reckoning. Dead
reckoning induces cumulative errors caused by wheel
slippage or rough surfaces that can quickly become
extremely severe. Therefor external sensors should be
used for adjustment of the robot position. In order to solve
this problem we make use of HRPS to help robot to
estimate its position. HRPS is a very fast robot
localization system using ultrasonic and infrared sensors.
When robot is moving it uses dead reckoning to measure
how much it has been moving. But for correcting the final
position it uses the HRPS.
HRPS system basically consists of one transmitter
module, which is, mounted on the mobile robot and some
(6 in our system) receiver modules, which are installed on
the fixed points in the ceiling of the room.
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The transmitter module consists of ultrasonic and
infrared transmitter arrays and can produce a burst of
both signals at the same time. The receiver module also
consists of ultrasonic and infrared sensors and necessary
circuit for detection and amplification of these signals.
When the transmitter module sends a couple of
ultrasonic and infrared pulses, the receiver module
receives the infrared signal almost at the same time (the
time of flight of light is negligible and the delay of
detecting circuit is considered as a fix value in
calculations). This signal starts a counter at the receiving
circuit. This counter will continue to count until the
reception of ultrasonic signal which, will stop the counter.
The value of this counter is proportional to the time of
flight of ultrasonic pulse and can be used to calculate the
distance between receiver and transmitter.
For measuring the position of the robot, the host
computer sends the "measure" command via wireless
LAN to the robot and robot produces a couple of
ultrasonic and infrared signals. Then the host computer
gets the data of receivers and uses them to calculate the
position of robot.
The location and the number of receivers are designed
so that at least 3 receivers will catch these signals from
robot. As the receivers are fixed at a plane with known
heights and also the transmitter is fixed in a known point
on the robot, the vertical distance between transmitter and
receivers are known in a priori. So the problem of
localization of robot can be done in two dimensions. As
shown in figure 3, the range (r) to any given receiver is
projected to the plane of transmitter (d), which maps out a
circular locus of possible positions.
d = ( r2-h2)1/2
(1)
The interception of two or more of these loci gives the
position of the robot.
The resolution of receiver circuit in measuring the range
is 1.5 cm and it can detect signals coming from a distance
up to 4 m. The positioning error is less than 5 cm.
5 Human localization using depth from focus
:
9
8
;
0
1
2
1
3
1
4
4
7
6
4
5
Fig.3. Robot positioning principle.
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This system estimates the 3D position of human’s
head and face by using the depth from focus method.
Traditionally there are two classes of approaches for depth
computation: active and passive. Active approaches use
infrared, ultrasonic or laser range finder for measuring the
distance while passive approaches place only a sensor in
the scene to acquire visual information. Examples for
passive approaches are depth from stereo, motion,
shading, texture, focus or defocus.
Focus interpretation is a valuable alternative to stereo
vision because it doesn' t require solving correspondence
for depth recovery [9].
Most imaging systems such as standard cameras can
only be in focus for one distance at a time. Surface
regions at this focal distance will produce sharp images;
surface at other distances will produce blurred images.
Surfaces at different distances can be brought into focus
one at a time by adjusting the power (focal length) of the
optical system. In the human visual system this is
accomplished by changing the shape of the lens. In
camera systems, they change the position of one or more
camera lenses.
Several researchers have proposed depth cues based
on focusing information [9,10]. A common technique
involves focusing the object on the image detector in the
camera. The distance is then determined from camera
setting. This technique is called depth from focusing [11].
The other common technique is depth from defocusing,
which calculates depth from degree of image blur [13].
In general, autofocusing techniques for video camera
maximize the high frequency components of an image by
adjusting the focusing lens. Focused images have more
high frequency components than defocused images of a
scene, and thus defocused images can be described as the
result of convolving the focused image with the blurring
function that plays the role of a low-pass filter.
In this system we use information from focusing ring
of an autofocus camera to measure depth information.
This ring provides depth information about the object in
the center of camera’s filed of view. By active control of
camera parameters we make the camera to autofocus on
the face. In this way we can compute the distance between
human face and camera using the data from focus ring
encoder. By having information about the absolute
position of robot and pan and tilt angles of camera we can
compute absolute position of the person in the room.
This method is quite simple and needs only calibration
of one camera parameter to obtain the relationship
between distance to object in focus and the autofocus lens
position. Details of implementation is reported in [7].
6 Tracking and Camera Control
In our system tracking keeps the object of interest
centered in the image by adequately moving the camera.
In order to use autofocus ability of camera, the camera
must have an appropriate zoom setting to make the target
large enough in the image. During depth measurement the
camera control policy is to change the zoom so that the
width of the face occupy approximately more than 50% of
the width of the camera image.
To obtain a close-up view of a particular subject, three
parameters need to be determined: the pan angle, the tilt
angle, and the zoom factor. Also for achieving high speed
tracking it is important to use both position and velocity
control.
A
B
Fig.4. a) Head is detected using head detection algorithm.
b) Head is brought inside the camera' s field of view by
changing pan, tilt and zoom factor.
In this system we use Fuzzy logic rules and reasoning
for control of speed and direction of camera. Fuzzy
controller has this advantage that we can do tracking
without need for camera calibration or modeling.
Input values are dx, dy (Fig.2.f) which determine the
difference between current position of face in the image
and image center point.
Controller has four outputs: Value of pan angle, value
of tilt angle, pan speed and tilt speed. Triangular
membership function is applied to the outputs. The
inference is of Mamdani inference engine type and center
of gravity defuzzification method is implemented.
The fuzzy controller consists of a rule base for speed and
direction control of camera. It contains rules for change
of tilt angle, change of pan angle and values of speed for
pan and tilt change. The rules are formulated in classical
logic form.
In order to detect features such as head motion and
face color in the image, the camera must have an
appropriate zoom setting. But for measuring distance,
system cannot use wide zoom and has to change the zoom
to provide closer view. Therefore the zooming is a
dynamic function in which system selects close view
when measuring the distance and select wide view when
tracking.
While tracking, the camera is moved in a saccadic way
in which no motion processing occurs during the camera
movement.
In the case of person with small motions, for example one
working with computer or watching TV or washing
dishes, robot can locate person’s position in the room with
less than 10 cm error. The measurable distance between
robot and human is from 90 cm up to 340 cm.
7 Navigation
The robot uses an internal grid-based map and its
sensory system to navigate in the environment. The
environment is considered to be structured with known
static objects and randomly placed furniture. The map
consists of doors, walls and static objects. As the robot
knows its initial position and final goal position and has a
map of the world, the path planning layer constructs a
path to the goal and begins to traverse that path.
Whenever the robot detects an obstacle in the path by its
obstacle avoidance layer, it executes an obstacle
exploration algorithm to find out boundaries of obstacle
and replans to find alternative path when it can not
proceed in the intended path.
Navigation is almost like subsumption method. It is
accomplished by several layers: a low level module that
avoids objects, and a higher level that moves the robot in
a particular direction, ignorant of obstacles. The
combination provides a simple way of getting from
current state to the goal.
It is supposed that human doesn' tmove during motion of
the robot and dynamics are mainly caused by
refurnishings.
Obstacle avoidance is based on ultrasonic sensor
information. There are 8 such sensors located around the
robot. When obstacles are found we update the map and a
new path may be computed from robot' s current position
to the goal. Therefore the planned paths may take into
account the placement of dynamic objects as soon as they
become relevant.
planning a path between robot and human we use
visibility graph approach [12]. A visibility graph is a
graph, G(V, E), where V is the set of all vertices of
obstacles within an environment including the coordinates
of the start and the destination, and E is the set of all
edges connecting any two vertices in V that do not pass
through any obstacles within the environment. Once a
visibility graph has been constructed for a given
environment, a search algorithm is used to find the
shortest path between starting and destination points.
The configuration of the robot position is specified by
a 3D vector, whose elements are the 2D position of the
front wheel and the orientation of the robot. The size of
robot is considered by setting a margin around each
obstacle. The size of the margin is set to half of the robot
width.
8 Experiment results
Our experiment was aimed to put all different modules
of system together in leading the robot to go near the
human with considering all uncertainty in robot position,
human position and obstacle perception. The experiment
has been conducted in a laboratory of 6x4 m2 with robot
moving with speed of v=0.2m/sec.
Figure 5 displays an example of a plan generated by
planner and executed path. Robot uses dead reckoning to
traverse each partial path and then at each stop point it
uses HRPS to measure its absolute position. Kalman filter
can be used for fusing data from two sensors. Due to the
crosstalk effect in ultrasonic sensors we have limited use
of HRPS to prevent confusion between HRPS and
obstacle avoidance system. This can be prevented if we
use other sensory system for obstacle avoidance.
HRPS provides very fast and exact information about
position of the robot but the error in robot' s direction is
considerable.
Positional information provided by human localization
system have enough accuracy for semi static human but
due to speed limitation of autofocus system it loses
accuracy for moving human but it is still applicable if we
can use low accuracy data for rough estimation.
9
Conclusion
7.1 Path planning
Path planning is a fundamental issue in robotics. The
purpose of a path planner is to compute a path, i.e. a
continuous sequence of configurations that leads the robot
to its goal. The existence of an absolute world model
allows for automatic path planning and execution, and for
subsequent route revisions in the event a new obstacle is
encountered.
The whole path of the mobile robot is expressed by an
assembly of straight line segments and turning angles. For
We have demonstrated a system, which can search for
the human inside room and guide a mobile robot to go
near to it. This is accomplished through the integration of
three key modules: Robot positioning by HRPS system,
Human detection by using color and motion information
and Human localization by depth estimation from focus
information.
Our system has application in robot and human interaction
but the method can be used for searching other landmarks
in the environment also. Currently we do not apply human
identification but we are going to use it to identify and
track a specific user in a multi person environment.
Fig. 5. a) A path is computed between robot and human
using visibility graph. b) Robot is guided to the first part
of path using dead reckoning, then robot is localized using
HRPS and a new path is computed. c) Robot is stopped
near the human using distance criteria.
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