This section describes the methods used to implement the perceptive autonomy layer that was initially described in Chapter 1. Perception is the process of understanding the environment based on measurements. Often the process involves the construction of models that are subsequently interpreted and improved or expanded. Although localization may enable mobility, it is environmental perception that enables a system to respond intelligently to what is out there – even when it differs from any expectations. Oftentimes, sufficiently intelligent behaviors emerge naturally based only on perception. Later, in Chapter 10, we will see that an ability to predict the future is sometimes necessary too.

Perception is a rapidly expanding area because it has many applications beyond robotics. For this reason, because space is limited, and because there are already many textbooks that concentrate on perception, this section will present only the bare essentials and only those that are most applicable to mobile robots.

Image Processing Operators and Algorithms

Perception and state estimation have a lot in common. Whereas state estimation estimates the state of the robot, perception estimates the state of the environment. Although states estimation tends to deal with signal variation over time, perception tends to deal with signal variation over space.