dynamic slam

**Dynamic SLAM** is the **localization and mapping paradigm designed for environments containing moving objects, where static-world assumptions no longer hold** - it separates dynamic and static elements to prevent trajectory and map corruption. **What Is Dynamic SLAM?** - **Definition**: SLAM system that detects and handles dynamic scene components during pose estimation. - **Core Problem**: Motion from people and vehicles can create false correspondences. - **Strategy**: Mask or model moving objects while preserving stable static landmarks. - **Outputs**: Robust static map, trajectory, and optionally dynamic object tracks. **Why Dynamic SLAM Matters** - **Real-World Robustness**: Most practical environments are not perfectly static. - **Pose Accuracy**: Removing dynamic outliers improves localization stability. - **Safety**: Better motion understanding supports autonomous navigation in crowds. - **Map Quality**: Prevents ghost artifacts from moving objects in persistent maps. - **System Reliability**: Reduces catastrophic tracking failures in urban scenes. **Dynamic Handling Methods** **Motion Segmentation**: - Identify moving regions via flow, semantics, or temporal residuals. - Exclude dynamic points from pose estimation. **Robust Estimation**: - Use RANSAC and robust losses to suppress outlier correspondences. - Preserve static structure constraints. **Dual-Map Approaches**: - Maintain static map plus dynamic object layer. - Support both localization and interaction planning. **How It Works** **Step 1**: - Detect dynamic regions and filter correspondences before geometric pose solve. **Step 2**: - Update static map with reliable features and optionally track dynamic agents separately. Dynamic SLAM is **the realism-aware SLAM evolution that preserves map integrity in moving-world conditions** - robust dynamic filtering is essential for dependable autonomy outside lab settings.