A Unifying View of Geometry, Semantics, and Data Association in SLAM
文章目錄 特徵提取 圖像特徵 語義特徵 代價函數 數據關聯 在視覺SLAM框架中加入語義特徵點，採用概率描述語義特徵點之間的對應關係，從而邊緣化數據關聯，優化相機位姿和語義地標點位置。 特徵提取 圖像特徵 由於選擇使用語義特徵來進行全局定位和迴環檢測，本文不關心圖像特徵的具體位置和全局的特徵關聯，只是使用特徵描述子來計算相鄰幀的位姿變換。 語義特徵 本文認爲語義特徵比圖像特徵數量少，但
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相關文章
- 1. The Future of Real-Time SLAM and 「Deep Learning vs SLAM」
- 2. Scyther-Semantics and verification of Security Protocol
- 3. [SLAM] a bite of SLAM
- 4. View and Data API Tips: Hide elements in viewer completely
- 5. Convert Geometry data into a Geography data in MS SQL Server
- 6. Using View and Data API with Meteor
- 7. Customized display of collection data in a PropertyGrid
- 8. Survey of 3D Map in SLAM: Localization and Navigation
- 9. Geometry and Appearances【轉】
- 10. 【Hector slam】A Flexible and Scalable SLAM System with Full 3D Motion Estimation