E-MLP: Effortless Online HD Map Construction with Linear Priors

Online High-definition map (HD-map) construction based on vehicle sensors has garnered widespread attention recently. While state-of-the-art methods achieve remarkable accuracy, most of them overlook the importance of inference speed and the inherent linear priors of map elements. Concretely, slow inference speed impacts the safety of autonomous vehicles, making it challenging for applications. Additionally, the absence of linear priors in map element predictions results in distorted or blurry outcomes. To address these issues, we propose E-MLP, an effortless online HD-map construction method that relies solely on camera sensors and incorporates the linear priors of map elements. Specifically, we first introduce a novel Principal Feature Analysis (PFA) module, designed to efficiently reduce the time cost of view transformation. Then, two thoughtfully crafted loss functions are introduced to incorporate the natural linear priors of map elements as constraints in the map construction process. Extensive experiments conducted on the nuScenes dataset revealed that, compared to the baseline method, our approach achieved a remarkable 34.9% increase in inference speed with virtually no loss in accuracy.