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.
Figures & tables
Figure 1: Overview of the camera pipeline. Different cone position estimation methods are performed in parallel and combined with an Extended Kalman Filter. Red and Green represent system inputs and outputs respectively, with blue representing pipeline sub-routines.
Figure 2: A ZED2 left camera frame with YOLOv8 bounding boxes. Solid bars above cone annotations represent the confidence of the YOLOv8 predictions.
Figure 3: General keypoint labels and their corresponding labels on a blue and yellow cone.
Figure 4: A high-level overview of the architecture used in our KPR model.
Figure 5: The training and validation loss values at each step plotted throughout model training.
Model
MSE
Root MSE
Norm ME
Std Dev
mAP
ResNet
6.3165
2.3458
0.0597
6.4299
0.42
UNet
3.4172
1.8486
0.0263
3.4550
0.83
Table 1: Metric Comparison of ResNet and UNet KPR NNs.
Figure 6: The mAP measures the distance between true and predicted keypoints indicated by a red line between points.
Figure 7: The predicted keypoints (in red) are inaccurate with respect to the ground truths (in green) due to the cone being partially cropped out by YOLOv8. The yellow line represents the disparity between the labels and inferences.
Figure 8: Confusion matrix showing integrated KPR pipeline detected classes against ground truth annotations on simulated data. Off-diagonal non-zero values indicate inaccuracies in the pipeline.
Figure 9: Birds-eye view displaying experimentally derived covariances for perception pipeline at varying distance bins. Blue points represent the error of network predictions, centered around the ground truth position. Red ellipses show covariances for each distance bin, with mean equal to the ground truth position.
Figure 10: The CPU and memory usage of the PC inside the car while running a ROS bag. Subfigure (a) shows the plot with no KPR method enabled. Here, the perception pipeline relies on YOLOv8 detection and estimation. The bottom figure (b) runs the pipeline with the KPR neural network enabled. The PC is under heavier computation in this scenario, so the CPU and memory usage become higher.
Technical University of Munich, Germany; School of Engineering & Design, Department of Mobility Systems Engineering, Institute of Automotive Technology and Munich Institute of Robotics and Machine Intelligence (MIRMI)