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 漏检补偿算法——提高SSD的召回率 首先基于卷积神经网络，构造一个联合先验知识的SSD目标检测器，在语义层检测新检测线程中的动态目标；然后，针对现有SSD目标检测网络的召回率较低
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相关文章
- 1. VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry 语义视觉定位与里程计
- 2. 阅读《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 辅助学习 && 2018 论文笔记
- 8. [SLAM] 01. "Simultaneous Localization and Mapping"
- 9. 泡泡一分钟：Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps
- 10. 「Deep Learning」Note on MaskLab