  In the RoboCup Small Size League (SSL), teams are encouraged to propose
solutions for executing basic soccer tasks inside the SSL field using only
embedded sensing information. Thus, this work proposes an embedded monocular
vision approach for detecting objects and estimating relative positions inside
the soccer field. Prior knowledge from the environment is exploited by assuming
objects lay on the ground, and the onboard camera has its position fixed on the
robot. We implemented the proposed method on an NVIDIA Jetson Nano and employed
SSD MobileNet v2 for 2D Object Detection with TensorRT optimization, detecting
balls, robots, and goals with distances up to 3.5 meters. Ball localization
evaluation shows that the proposed solution overcomes the currently used SSL
vision system for positions closer than 1 meter to the onboard camera with a
Root Mean Square Error of 14.37 millimeters. In addition, the proposed method
achieves real-time performance with an average processing speed of 30 frames
per second.
