Where am I? Sensors and Methods for Mobile Robot Positioning

176 Part II Systems and Methods for Mobile Robot Positioning
Fukui [1981] used a diamond-shaped landmark and applied a least-squares method to find line
segments in the image plane. Borenstein [1987] used a black rectangle with four white dots in the
corners. Kabuka and Arenas [1987] used a half-white and half-black circle with a unique bar-code
for each landmark. Magee and Aggarwal [1984] used a sphere with horizontal and vertical
calibration circles to achieve three-dimensional localization from a single image. Other systems use
reflective material patterns and strobed light to ease the segmentation and parameter extraction
[Lapin, 1992; Mesaki and Masuda, 1992]. There are also systems that use active (i.e., LED) patterns
to achieve the same effect [Fleury and Baron, 1992].
The accuracy achieved by the above methods depends on the accuracy with which the geometric
parameters of the landmark images are extracted from the image plane, which in turn depends on
the relative position and angle between the robot and the landmark. In general, the accuracy
decreases with the increase in relative distance. Normally there is a range of relative angles in which
good accuracy can be achieved, while accuracy drops significantly once the relative angle moves
out of the “good” region.
There is also a variety of landmarks used in conjunction with non-vision sensors. Most often used
are bar-coded reflectors for laser scanners. For example, currently ongoing work by Everett on the
Mobile Detection Assessment and Response System (MDARS) [DeCorte, 1994] uses retro-reflectors,
and so does the commercially available system from Caterpillar on their Self-Guided Vehicle [Gould,
1990]. The shape of these landmarks is usually unimportant. By contrast, a unique approach taken
by Feng et al. [1992] used a circular landmark and applied an optical Hough transform to extract the
parameters of the ellipse on the image plane in real time.
7.2.1 Global Vision
Yet another approach is the so-called global vision that refers to the use of cameras placed at fixed
locations in a workspace to extend the local sensing available on board each AGV [Kay and Luo,
1993]. Figure 7.4 shows a block diagram of the processing functions for vehicle control using global
vision.
In global vision methods, characteristic points forming a pattern on the mobile robot are identified
and localized from a single view. A probabilistic method is used to select the most probable matching
according to geometric characteristics of those percepts. From this reduced search space a
prediction-verification loop is applied to identify and to localize the points of the pattern [Fleury and
Baron, 1992]. One advantage of this approach is that it allows the operator to monitor robot
operation at the same time.
7.3 Artificial Landmark Navigation Systems
Many systems use retroreflective barcodes as artificial landmarks, similar to the ones used in beacon
navigation systems. However, in this book we distinguish between retroreflective bar-codes used as
artificial landmarks and retroreflective poles used as “beacons.” The reason is that if retroreflective
markers (with or without bar-code) are attached to the walls of a room and their function is merely
to aid in determining the location of the wall, then these markers do not