Page 190 - Introduction to Autonomous Mobile Robots

Perception

Figure 4.48 175
Examples of adaptive floor plane extraction. The trapezoidal polygon identifies the floor sampling
region.

by looking at the appropriate histogram counts for the qualities of the target pixel. For
example, if the target pixel has a hue that never occurred in the “floor sample,” then the
corresponding hue histogram will have a count of zero. When a pixel references a histo-
gram value below a predefined threshold, that pixel is classified as an obstacle.
Figure 4.48 shows an appearance-based floor plane extraction algorithm operating on
both indoor and outdoor images [151]. Note that, unlike the static floor extraction algo-
rithm, the adaptive algorithm is able to successfully classify a human shadow due to the
adaptive histogram representation. An interesting extension of the work has been to not use
the static floor sample assumption, but rather to record visual history and to use, as the floor
sample, only the portion of prior visual images that has successfully rolled under the robot
during mobile robot motion.
Appearance-based extraction of the floor plane has been demonstrated on both indoor
and outdoor robots for real-time obstacle avoidance with a bandwidth of up to 10 Hz.
Applications include robotics lawn mowing, social indoor robots, and automated electric
wheelchairs.

4.3.2.2 Whole-image features
A single visual image provides so much information regarding a robot’s immediate sur-
roundings that an alternative to searching the image for spatially localized features is to
make use of the information captured by the entire image to extract a whole-image feature.