  With the rise of automation, unmanned vehicles became a hot topic both as
commercial products and as a scientific research topic. It composes a
multi-disciplinary field of robotics that encompasses embedded systems, control
theory, path planning, Simultaneous Localization and Mapping (SLAM), scene
reconstruction, and pattern recognition. In this work, we present our
exploratory research of how sensor data fusion and state-of-the-art machine
learning algorithms can perform the Embodied Artificial Intelligence (E-AI)
task called Visual Semantic Navigation. This task, a.k.a Object-Goal Navigation
(ObjectNav) consists of autonomous navigation using egocentric visual
observations to reach an object belonging to the target semantic class without
prior knowledge of the environment. Our method reached fourth place on the
Habitat Challenge 2021 ObjectNav on the Minival phase and the Test-Standard
Phase.
