論文《FLAME:Feature-Likeilhood Based Mapping and Localization for Autonomous Vehicles》閱讀總結
摘要 準確的車輛定位是自動駕駛導航最關鍵也是最基本的任務。雖然基於密集3D點雲的地圖可以進行準確地定位，但是在城市規模的環境中使用時，它們會帶來巨大的存儲負擔和傳輸負擔。 在本文中，我們提出了一種用於LiDAR地圖的高度壓縮表示，以及一種針對車載LiDAR掃描的高效且強大的實時對齊算法(alignment algorithm)。所提出的建圖框架（我們稱爲特徵似然獲取地圖仿真（Feature Lik
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相關文章
- 1. 論文閱讀CVPR2019——Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 2. 閱讀《A Survey of Monocular Simultaneous Localization and Mapping》
- 3. 論文閱讀：A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles
- 4. 論文筆記：Planning and Decision-Making for Autonomous Vehicles
- 5. 論文閱讀-《Learning Deep Features for Discriminative Localization》
- 6. 【文獻閱讀】Probabilistic Terrain Mapping for Mobile Robots With Uncertain Localization
- 7. [SLAM] 01. "Simultaneous Localization and Mapping"
- 8. 論文閱讀筆記：Learning Deep Features for Discriminative Localization
- 9. 論文閱讀之identity mapping
- 10. 論文翻譯《Computer Vision for Autonomous Vehicles Problems, Datasets and State-of-the-Art》(第八章）