Comparison of Visual SLAM
🎯 Visual Monocular SLAM Comparison
| Method | Approach | Map Type | Loop Closure | Speed | Notes |
|---|---|---|---|---|---|
| ORB-SLAM3 (Mono) | Feature-based | Sparse | ✅ Yes | Medium | State-of-the-art, robust, supports large-scale mapping |
| ORB-SLAM2 | Feature-based | Sparse | ✅ Yes | Medium | Widely used, reliable for small to medium environments |
| REBVO | Edge / Semi-direct VO | Sparse | ❌ No | ⚡ Fast | Lightweight odometry, drift over time, good for embedded devices |
| LSD-SLAM | Direct | Semi-dense | ✅ Yes | Medium | Semi-dense map, works in low-texture scenes |
| DSO | Direct Sparse | Sparse | ❌ No | Medium | High precision, sensitive to lighting changes |
| SVO | Semi-direct | Sparse | ❌ No | ⚡ Fast | Minimal computational cost, real-time tracking |
| PTAM | Feature-based | Sparse | Partial | Medium | Classic method, separates tracking & mapping, mostly for research |
Legend
- ✅ Loop closure supported
- ❌ Loop closure not supported
- ⚡ Fast / real-time oriented
Key Takeaways:
- 🟢 For accurate mapping & loop closure: ORB-SLAM2/3
- 🟡 For lightweight odometry: REBVO, SVO
- 🔵 For semi-dense or research exploration: LSD-SLAM, DSO, PTAM
🔥 Curated Video Resources
Here are several useful YouTube tutorials and demos relevant to autonomous perception, computer vision, robotics, and depth understanding.