Learnable Resized and Laplacian-Filtered U-Net: Better Road Marking Extraction and Classification on Sparse-Point-Cloud-Derived Imagery

Abstract

High-definition (HD) maps for autonomous driving rely on data from mobile mapping systems (MMS), but the high cost of MMS sensors has led researchers to explore cheaper alternatives like low-cost LiDAR sensors. While cost effective, these sensors produce sparser point clouds, leading to poor feature representation and degraded performance in deep learning techniques, such as convolutional neural networks (CNN), for tasks like road marking extraction and classification, which are essential for HD map generation. Examining common image segmentation workflows and the structure of U-Net, a CNN, reveals a source of performance loss in the succession of resizing operations, which further diminishes the already poorly represented features. Addressing this, we propose improving U-Net’s ability to extract and classify road markings from sparse-point-cloud-derived images by introducing a learnable resizer (LR) at the input stage and learnable resizer blocks (LRBs) throughout the network, thereby mitigating feature and localization degradation from resizing operations in the deep learning framework. Additionally, we incorporate Laplacian filters (LFs) to better manage activations along feature boundaries. Our analysis demonstrates significant improvements, with F1-scores increasing from below 20% to above 75%, showing the effectiveness of our approach in improving road marking extraction and classification from sparse-point-cloud-derived imagery.

Keywords

• low-cost LiDAR
• road marking extraction and classification
• deep learning