Accurate cone localization in 3D space is essential in autonomous racing for precise navigation around the track. Approaches that rely on traditional computer vision algorithms are sensitive to environmental variations, and neural networks are often trained on limited data and are infeasible to run in real time. We present a UNet-based neural network for keypoint detection on cones, leveraging the largest custom-labeled dataset we have assembled. Our approach enables accurate cone position estimation and the potential for color prediction. Our model achieves substantial improvements in keypoint accuracy over conventional methods. Furthermore, we leverage our predicted keypoints in the perception pipeline and evaluate the end-to-end autonomous system. Our results show high-quality performance across all metrics, highlighting the effectiveness of this approach and its potential for adoption in competitive autonomous racing systems.

翻译：在自动驾驶赛车中，三维空间中的精确锥桶定位对于赛道精准导航至关重要。依赖传统计算机视觉算法的方法对环境变化敏感，而神经网络通常基于有限数据训练且难以实时运行。我们提出了一种基于UNet的神经网络，用于锥桶关键点检测，并利用了我们所构建的最大规模自定义标注数据集。我们的方法能够实现精确的锥桶位置估计，并具备颜色预测的潜力。相较于传统方法，我们的模型在关键点精度上取得了显著提升。此外，我们将预测的关键点应用于感知流程中，并对端到端自动驾驶系统进行了评估。我们的结果显示，在所有指标上均表现出高性能，突显了该方法的有效性及其在竞技性自动驾驶赛车系统中应用的潜力。