Keyframe selection is the policy that chooses which frames become map anchors to balance information richness, computational cost, and map size - selecting informative keyframes is essential for stable SLAM and efficient optimization.

What Is Keyframe Selection?

• Definition: Decide when to insert a new keyframe based on motion, overlap, and tracking quality criteria.
• Purpose: Avoid storing redundant frames while preserving enough coverage for relocalization and mapping.
• Inputs: Pose change, feature novelty, uncertainty, and scene dynamics.
• Outputs: Sparse set of representative frames used in map and backend optimization.

Why Keyframe Selection Matters

• Efficiency: Fewer redundant keyframes reduce memory and compute burden.
• Optimization Quality: Better keyframe distribution improves graph conditioning.
• Relocalization Strength: Representative landmarks increase successful place matching.
• Real-Time Performance: Controls backend workload growth over long missions.
• Map Longevity: Good keyframe policies support robust long-term operation.

Selection Strategies

Motion Thresholding:

• Insert keyframe after sufficient translation or rotation.
• Simple and effective baseline.

Information Gain:

• Add keyframe when new observations provide significant scene novelty.
• Reduces overlap redundancy.

Quality-Aware Policy:

• Trigger keyframe when tracking uncertainty rises.
• Improves robustness in difficult segments.

How It Works

Step 1:

• Evaluate current frame against latest keyframe using motion and overlap metrics.

Step 2:

• Insert frame as keyframe if thresholds or uncertainty rules are satisfied; otherwise continue tracking.

Keyframe selection is the data-budget control mechanism that keeps SLAM maps informative without becoming computationally unmanageable - careful policy design improves both speed and global accuracy.