用UNet模型精准定位赛道锥桶3D位置，提升自动驾驶赛车感知能力
UNet-Based Keypoint Regression for 3D Cone Localization in Autonomous Racing
- 基于UNet的关节点检测网络，利用自建大规模标注数据集
- 关键点定位精度显著优于传统方法，支持颜色预测
- 端到端系统验证表现优异，适合竞速类自动驾驶场景
在自动驾驶赛车中，精确获取锥桶在三维空间中的位置对精准绕行赛道至关重要。依赖传统计算机视觉算法的方法易受环境变化影响，而现有神经网络模型常因训练数据有限且难以实时运行。本文提出一种基于UNet的锥桶关节点检测网络，利用我们构建的最大规模自定义标注数据集进行训练。该方法可实现高精度的锥桶位置估计，并具备颜色预测潜力。实验表明，模型在关键点定位精度上相比传统方法有显著提升。进一步地，我们将预测的关键点集成至感知流程，评估了端到端自动驾驶系统的性能。所有指标均表现出色，验证了该方法的有效性及其在竞赛级自动驾驶系统中的应用前景。
原文摘要 · Abstract (English)
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.
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