UNet-Based Keypoint Regression for 3D Cone Localization in Autonomous Racing
This addresses the need for precise navigation in autonomous racing, though it appears incremental as it builds on existing UNet architectures for a specific domain.
The paper tackles the problem of accurate 3D cone localization for autonomous racing by presenting a UNet-based neural network for keypoint detection, achieving substantial improvements in keypoint accuracy over conventional methods.
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.