  To autonomously navigate in real-world environments, special in search and
rescue operations, Unmanned Aerial Vehicles (UAVs) necessitate comprehensive
maps to ensure safety. However, the prevalent metric map often lacks semantic
information crucial for holistic scene comprehension. In this paper, we
proposed a system to construct a probabilistic metric map enriched with object
information extracted from the environment from RGB-D images. Our approach
combines a state-of-the-art YOLOv8-based object detection framework at the
front end and a 2D SLAM method - CartoGrapher at the back end. To effectively
track and position semantic object classes extracted from the front-end
interface, we employ the innovative BoT-SORT methodology. A novel association
method is introduced to extract the position of objects and then project it
with the metric map. Unlike previous research, our approach takes into reliable
navigating in the environment with various hollow bottom objects. The output of
our system is a probabilistic map, which significantly enhances the map's
representation by incorporating object-specific attributes, encompassing class
distinctions, accurate positioning, and object heights. A number of experiments
have been conducted to evaluate our proposed approach. The results show that
the robot can effectively produce augmented semantic maps containing several
objects (notably chairs and desks). Furthermore, our system is evaluated within
an embedded computer - Jetson Xavier AGX unit to demonstrate the use case in
real-world applications.
