Page 168 - Introduction to Autonomous Mobile Robots

Perception

a) b) 153

Figure 4.35
Environment representation and modeling: (a) feature based (continuous metric); (b) occupancy grid
(discrete metric). Courtesy of Sjur Vestli.

Available sensors. Obviously, the specific sensors and sensor uncertainty of the robot
impacts the appropriateness of various features. Armed with a laser rangefinder, a robot is
well qualified to use geometrically detailed features such as corner features owing to the
high-quality angular and depth resolution of the laser scanner. In contrast, a sonar-equipped
robot may not have the appropriate tools for corner feature extraction.

Computational power. Vision-based feature extraction can effect a significant computa-
tional cost, particularly in robots where the vision sensor processing is performed by one
of the robot’s main processors.

Environment representation. Feature extraction is an important step toward scene inter-
pretation, and by this token the features extracted must provide information that is conso-
nant with the representation used for the environmental model. For example, nongeometric
vision-based features are of little value in purely geometric environmental models but can
be of great value in topological models of the environment. Figure 4.35 shows the applica-
tion of two different representations to the task of modeling an office building hallway.
Each approach has advantages and disadvantages, but extraction of line and corner features
has much more relevance to the representation on the left. Refer to chapter 5, section 5.5
for a close look at map representations and their relative trade-offs.