论文《FLAME:Feature-Likeilhood Based Mapping and Localization for Autonomous Vehicles》阅读总结
摘要 准确的车辆定位是自动驾驶导航最关键也是最基本的任务。虽然基于密集3D点云的地图可以进行准确地定位，但是在城市规模的环境中使用时，它们会带来巨大的存储负担和传输负担。 在本文中，我们提出了一种用于LiDAR地图的高度压缩表示，以及一种针对车载LiDAR扫描的高效且强大的实时对齐算法(alignment algorithm)。所提出的建图框架（我们称为特征似然获取地图仿真（Feature Lik
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相关文章
- 1. 论文阅读CVPR2019——Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 2. 阅读《A Survey of Monocular Simultaneous Localization and Mapping》
- 3. 论文阅读：A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles
- 4. 论文笔记：Planning and Decision-Making for Autonomous Vehicles
- 5. 论文阅读-《Learning Deep Features for Discriminative Localization》
- 6. 【文献阅读】Probabilistic Terrain Mapping for Mobile Robots With Uncertain Localization
- 7. [SLAM] 01. "Simultaneous Localization and Mapping"
- 8. 论文阅读笔记：Learning Deep Features for Discriminative Localization
- 9. 论文阅读之identity mapping
- 10. 论文翻译《Computer Vision for Autonomous Vehicles Problems, Datasets and State-of-the-Art》(第八章）