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

arXiv: https://arxiv.org/abs/2207.09851 · HTML: https://mlsanity.com/paper/2207.09851/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices](https://mlsanity.com/paper/2108.09457/) (2021)
- [Real-Time Monocular Human Depth Estimation and Segmentation on Embedded Systems](https://mlsanity.com/paper/2108.10506/) (2021)
- [6D Object Pose Estimation using Keypoints and Part Affinity Fields](https://mlsanity.com/paper/2107.02057/) (2021)
- [Dataset and Benchmarking of Real-Time Embedded Object Detection for RoboCup SSL](https://mlsanity.com/paper/2106.14597/) (2021)
- [ROBO: Robust, Fully Neural Object Detection for Robot Soccer](https://mlsanity.com/paper/1910.10949/) (2019)
- [Detecting soccer balls with reduced neural networks: a comparison of multiple architectures under constrained hardware scenarios](https://mlsanity.com/paper/2009.13684/) (2020)

## Built on by (most cited first, 1 of 1)

- [RoboCIn Small Size League Extended Team Description Paper for RoboCup 2023](https://mlsanity.com/paper/2307.10018/) (2023)

## Abstract (arXiv)

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.