HDMapNet: An Online HD Map Construction and Evaluation Framework. (arXiv:2107.06307v1 [cs.CV])

High-definition map (HD map) construction is a crucial problem for autonomous
driving. This problem typically involves collecting high-quality point clouds,
fusing multiple point clouds of the same scene, annotating map elements, and
updating maps constantly. This pipeline, however, requires a vast amount of
human efforts and resources which limits its scalability. Additionally,
traditional HD maps are coupled with centimeter-level accurate localization
which is unreliable in many scenarios. In this paper, we argue that online map
learning, which dynamically constructs the HD maps based on local sensor
observations, is a more scalable way to provide semantic and geometry priors to
self-driving vehicles than traditional pre-annotated HD maps. Meanwhile, we
introduce an online map learning method, titled HDMapNet. It encodes image
features from surrounding cameras and/or point clouds from LiDAR, and predicts
vectorized map elements in the bird’s-eye view. We benchmark HDMapNet on the
nuScenes dataset and show that in all settings, it performs better than
baseline methods. Of note, our fusion-based HDMapNet outperforms existing
methods by more than 50% in all metrics. To accelerate future research, we
develop customized metrics to evaluate map learning performance, including both
semantic-level and instance-level ones. By introducing this method and metrics,
we invite the community to study this novel map learning problem. We will
release our code and evaluation kit to facilitate future development.

Source: https://arxiv.org/abs/2107.06307

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