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

Sistema de Navega\c{c}\~ao Aut\^onomo Baseado em Vis\~ao Computacional