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Mobile Robot Localization
with respect to target humans is equally important. Its localization task can include identi-
fying humans using its sensor array, then computing its relative position to the humans.
Furthermore, during the cognition step a robot will select a strategy for achieving its goals.
If it intends to reach a particular location, then localization may not be enough. The robot
may need to acquire or build an environmental model, a map, that aids it in planning a path
to the goal. Once again, localization means more than simply determining an absolute pose
in space; it means building a map, then identifying the robot’s position relative to that map.
Clearly, the robot’s sensors and effectors play an integral role in all the above forms of
localization. It is because of the inaccuracy and incompleteness of these sensors and effec-
tors that localization poses difficult challenges. This section identifies important aspects of
this sensor and effector suboptimality.

5.2.1 Sensor noise
Sensors are the fundamental robot input for the process of perception, and therefore the
degree to which sensors can discriminate the world state is critical. Sensor noise induces a
limitation on the consistency of sensor readings in the same environmental state and, there-
fore, on the number of useful bits available from each sensor reading. Often, the source of
sensor noise problems is that some environmental features are not captured by the robot’s
representation and are thus overlooked.
For example, a vision system used for indoor navigation in an office building may use
the color values detected by its color CCD camera. When the sun is hidden by clouds, the
illumination of the building’s interior changes because of the windows throughout the
building. As a result, hue values are not constant. The color CCD appears noisy from the
robot’s perspective as if subject to random error, and the hue values obtained from the CCD
camera will be unusable, unless the robot is able to note the position of the sun and clouds
in its representation.
Illumination dependence is only one example of the apparent noise in a vision-based
sensor system. Picture jitter, signal gain, blooming, and blurring are all additional sources
of noise, potentially reducing the useful content of a color video image.
Consider the noise level (i.e., apparent random error) of ultrasonic range-measuring sen-
sors (e.g., sonars) as discussed in section 4.1.2.3. When a sonar transducer emits sound
toward a relatively smooth and angled surface, much of the signal will coherently reflect
away, failing to generate a return echo. Depending on the material characteristics, a small
amount of energy may return nonetheless. When this level is close to the gain threshold of
the sonar sensor, then the sonar will, at times, succeed and, at other times, fail to detect the
object. From the robot’s perspective, a virtually unchanged environmental state will result
in two different possible sonar readings: one short and one long.
The poor signal-to-noise ratio of a sonar sensor is further confounded by interference
between multiple sonar emitters. Often, research robots have between twelve and forty-