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2023 | OriginalPaper | Chapter

An Embedded Monocular Vision Approach for Ground-Aware Objects Detection and Position Estimation

Authors : João G. Melo, Edna Barros

Published in: RoboCup 2022: Robot World Cup XXV

Publisher: Springer International Publishing

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Abstract

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 m. Ball localization evaluation shows that the proposed solution overcomes the currently used SSL vision system for positions closer than 1 m to the onboard camera with a Root Mean Square Error of 14.37 mm. In addition, the proposed method achieves real-time performance with an average processing speed of 30 frames per second.

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previous chapter Evaluating Action-Based Temporal Planners Performance in the RoboCup Logistics League

next chapter Adaptive Team Behavior Planning Using Human Coach Commands

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Title: An Embedded Monocular Vision Approach for Ground-Aware Objects Detection and Position Estimation
Authors: João G. Melo
Edna Barros
Publisher: Springer International Publishing
Book: RoboCup 2022: Robot World Cup XXV
Print ISBN: 978-3-031-28468-7

Electronic ISBN: 978-3-031-28469-4

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-28469-4_9

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