Page 188 - Introduction to Autonomous Mobile Robots

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Perception
Floor plane extraction. Obstacle avoidance is one of the basic tasks required of most
mobile robots. Range-based sensors provide effective means for identifying most types of
obstacles facing a mobile robot. In fact, because they directly measure range to objects in
the world, range-based sensors such as ultrasonic and laser rangefinders are inherently well
suited for the task of obstacle detection. However, each ranging sensor has limitations.
Ultrasonics have poor angular resolution and suffer from coherent reflection at shallow
angles. Most laser rangefinders are 2D, only detecting obstacles penetrating a specific
sensed plane. Stereo vision and depth from focus require the obstacles and floor plane to
have texture in order to enable correspondence and blurring respectively.
In addition to each individual shortcoming, range-based obstacle detection systems will
have difficulty detecting small or flat objects that are on the ground. For example, a vacuum
cleaner may need to avoid large, flat objects, such as paper or money left on the floor. In
addition, different types of floor surfaces cannot easily be discriminated by ranging. For
example, a sidewalk-following robot will have difficulty discriminating grass from pave-
ment using range sensing alone.
Floor plane extraction is a vision-based approach for identifying the traversable portions
of the ground. Because it makes use of edges and color in a variety of implementations,
such obstacle detection systems can easily detect obstacles in cases that are difficult for tra-
ditional ranging devices.
As is the case with all vision-based algorithms, floor plane extraction succeeds only in
environments that satisfy several important assumptions:
• Obstacles differ in appearance from the ground.
• The ground is flat and its angle to the camera is known.
• There are no overhanging obstacles.

The first assumption is a requirement in order to discriminate the ground from obstacles
using its appearance. A stronger version of this assumption, sometimes invoked, states that
the ground is uniform in appearance and different from all obstacles. The second and third
assumptions allow floor plane extraction algorithms to estimate the robot’s distance to
obstacles detected.

Floor plane extraction in artificial environments. In a controlled environment, the
floor, walls and obstacles can be designed so that the walls and obstacles appear signifi-
cantly different from the floor in a camera image. Shakey, the first autonomous robot devel-
oped from 1966 through 1972 at SRI, used vision-based floor plane extraction in a
manufactured environment for obstacle detection [115]. Shakey’s artificial environment
used textureless, homogeneously white floor tiles. Furthermore, the base of each wall was
painted with a high-contrast strip of black paint and the edges of all simple polygonal obsta-
cles were also painted black.