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.