Page 167 - Introduction to Autonomous Mobile Robots

Environment 152 sensing treatment extraction pretation Chapter 4
scene
feature
signal
inter-

Figure 4.34
The perceptual pipeline: from sensor readings to knowledge models.

between low-level features (geometric primitives) like lines, circles, or polygons, and high-
level features (objects) such as edges, doors, tables, or a trash can. At one extreme, raw
sensor data provide a large volume of data, but with low distinctiveness of each individual
quantum of data. Making use of raw data has the potential advantage that every bit of infor-
mation is fully used, and thus there is a high conservation of information. Low-level fea-
tures are abstractions of raw data, and as such provide a lower volume of data while
increasing the distinctiveness of each feature. The hope, when one incorporates low-level
features, is that the features are filtering out poor or useless data, but of course it is also
likely that some valid information will be lost as a result of the feature extraction process.
High-level features provide maximum abstraction from the raw data, thereby reducing the
volume of data as much as possible while providing highly distinctive resulting features.
Once again, the abstraction process has the risk of filtering away important information,
potentially lowering data utilization.
Although features must have some spatial locality, their geometric extent can range
widely. For example, a corner feature inhabits a specific coordinate location in the geomet-
ric world. In contrast, a visual “fingerprint” identifying a specific room in an office building
applies to the entire room, but has a location that is spatially limited to the one particular
room.
In mobile robotics, features play an especially important role in the creation of environ-
mental models. They enable more compact and robust descriptions of the environment,
helping a mobile robot during both map-building and localization. When designing a
mobile robot, a critical decision revolves around choosing the appropriate features for the
robot to use. A number of factors are essential to this decision:

Target environment. For geometric features to be useful, the target geometries must be
readily detected in the actual environment. For example, line features are extremely useful
in office building environments due to the abundance of straight wall segments, while the
same features are virtually useless when navigating Mars.