Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning
Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment 漏檢補償算法——提高SSD的召回率 首先基於卷積神經網絡，構造一個聯合先驗知識的SSD目標檢測器，在語義層檢測新檢測線程中的動態目標；然後，針對現有SSD目標檢測網絡的召回率較低
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相關文章
- 1. VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry 語義視覺定位與里程計
- 2. 閱讀《A Survey of Monocular Simultaneous Localization and Mapping》
- 3. Point-based Semantic Monocular SLAM
- 4. A Review on Deep Learning Techniques Applied to Semantic Segmentation
- 5. The Future of Real-Time SLAM and 「Deep Learning vs SLAM」
- 6. Summary of defect detection algorithms based on deep learning
- 7. VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry 輔助學習 && 2018 論文筆記
- 8. [SLAM] 01. "Simultaneous Localization and Mapping"
- 9. 泡泡一分鐘：Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps
- 10. 「Deep Learning」Note on MaskLab