Road Mapping and Localization using Sparse Semantic Visual Features
arXiv:2108.05047
Abstract
We present a novel method for visual mapping and localization for autonomous vehicles, by extracting, modeling, and optimizing semantic road elements. Specifically, our method integrates cascaded deep models to detect standardized road elements instead of traditional point features, to seek for improved pose accuracy and map representation compactness. To utilize the structural features, we model road lights and signs by their representative deep keypoints for skeleton and boundary, and parameterize lanes via piecewise cubic splines. Based on the road semantic features, we build a complete pipeline for mapping and localization, which includes a) image processing front-end, b) sensor fusion strategies, and c) optimization backend. Experiments on public datasets and our testing platform have demonstrated the effectiveness and advantages of our method by outperforming traditional approaches.
arXiv commentPublished at IEEE ROBOTICS AND AUTOMATION LETTERS
References in corpus (6)
- Adam: A Method for Stochastic Optimization
- VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
- Visual-Inertial Monocular SLAM with Map Reuse
- maplab: An Open Framework for Research in Visual-inertial Mapping and Localization
- Pose Estimation for Ground Robots: On Manifold Representation, Integration, Re-Parameterization, and Optimization
- Fully automatic structure from motion with a spline-based environment representation