Stereo Slam

Stereo SLAM is the visual SLAM approach that uses two synchronized cameras with known baseline to estimate depth directly and preserve metric scale - this reduces ambiguity and improves robustness compared with monocular setups.

What Is Stereo SLAM?

• Definition: SLAM pipeline using left-right image pairs plus temporal tracking.
• Scale Advantage: Known baseline enables direct depth and absolute scale recovery.
• Map Quality: Better initial landmark depth than monocular triangulation.
• Runtime Components: Stereo matching, visual odometry, mapping, and loop closure.

Why Stereo SLAM Matters

• Metric Reliability: Maintains physically meaningful distances without extra sensors.
• Faster Initialization: Immediate depth estimates reduce startup fragility.
• Tracking Robustness: More stable under pure rotational motions than monocular.
• Navigation Utility: Strong fit for mobile robots and autonomous platforms.
• Operational Tradeoff: Higher compute due to stereo matching stage.

Stereo SLAM Pipeline

Depth Estimation:
- Compute disparity between synchronized camera views.
- Convert disparity to depth using calibrated baseline.

Temporal Tracking:
- Track landmarks and estimate pose over time.
- Fuse stereo depth with temporal correspondences.

Global Optimization:
- Detect loop closures and optimize pose graph for consistency.
- Update map landmarks after global correction.

How It Works

Step 1:
- Generate depth from stereo pairs and initialize map with metric landmarks.

Step 2:
- Run temporal pose tracking and periodic global optimization to maintain consistency.

Stereo SLAM is a strong metric-accurate localization and mapping solution that balances visual richness with reliable scale estimation - it remains a practical default when dual-camera hardware is available.