Cooperating robots for mapping tasks with a multilayer perceptron

This paper proposes a Multilayer Perceptron application in a FastSLAM solution for landmarks update aimed at improve computational performance. The algorithm extracts landmarks from visual sensors, and uses a common features map for two robots. Both activities (i. e. location for the robots and exploration task) are coordinate by central agent. Landmarks update a than are done with a neural network using an Extended Kalman Filter, as usual in several research works in literature. Experiments have shown that generated errors obtained are equivalent in both methods. However, processing time is 10-12 times lower when using our proposed method. This contributes to attend real time requirements during autonomous robot operation.