Meaningful Maps With Object-Oriented Semantic Mapping
基於RGB-D數據的語義建圖（SLAM幫助語義）。具體過程如下 首先利用SSD執行單幀RGB圖片（2D）物體檢測。 接着基於檢測結果和深度信息，利用3D分割算法進一步分割3D點雲。 最終利用ORB-SLAM2的相機位姿信息將每幀的3D點雲分割結果合併到地圖中，建立語義點雲地圖。 特色： 非物體只維護點雲，物體維護點雲和類別標號。 點雲分別在每個觀測幀中維護。好處是閉環後點雲也可以跟着觀測幀的位姿變
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相關文章
- 1. Meaningful Maps With Object-Oriented Semantic Mapping 【summary】
- 2. Meaningful Maps With Object-Oriented Semantic Mapping 論文筆記
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- 5. 論文解析 - SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks
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- 8. Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping ,2019
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- 10. 泡泡一分鐘：Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps