  Autonomous robots are used as the tool to solve many kinds of problems, such
as environmental mapping and monitoring. Either for adverse conditions related
to the human presence or even for the need to reduce costs, it is certain that
many efforts have been made to develop robots with an increasingly high level
of autonomy. They must be capable of locomotion through dynamic environments,
without human operators or assistant systems' help. It is noted, thus, that the
form of perception and modeling of the environment becomes significantly
relevant to navigation. Among the main sensing methods are those based on
vision. Through this, it is possible to create highly-detailed models about the
environment, since many characteristics can be measured, such as texture,
color, and illumination. However, the most accurate vision-based navigation
techniques are computationally expensive to run on low-cost mobile platforms.
Therefore, the goal of this work was to develop a low-cost robot, controlled by
a Raspberry Pi, whose navigation system is based on vision. For this purpose,
the strategy used consisted in identifying obstacles via optical flow pattern
recognition. Through this signal, it is possible to infer the relative
displacement between the robot and other elements in the environment. Its
estimation was done using the Lucas-Kanade algorithm, which can be executed by
the Raspberry Pi without harming its performance. Finally, an SVM based
classifier was used to identify patterns of this signal associated with
obstacles movement. The developed system was evaluated considering its
execution over an optical flow pattern dataset extracted from a real navigation
environment. In the end, it was verified that the processing frequency of the
system was superior to the others. Furthermore, its accuracy and acquisition
cost were, respectively, higher and lower than most of the cited works.
