Graph-based SLAM Guide 2026: Fundamentals, Optimization, and Examples
Graph-based SLAM Guide 2026: Fundamentals, Optimization, and Examples Master Graph-based SLAM for modern robotics. Learn about Factor Graphs, Loop Closure, and why Pose-Graph Optimization is the backbone of autonomous vehicle mapping in 2026. 1. Introduction: From Filtering to Smoothing The fundamental shift in SLAM over the last decade has been the move from Filtering (like EKF) to Smoothing (Graph-based). Filtering: Only maintains the current state. Once a landmark is passed, its specific correlation to the robot's starting point is often simplified or lost to save memory. Graph-based (Smoothing): Maintains the entire history of the robot's trajectory. It treats every pose and landmark as a node in a massive web, allowing the system to "smooth out" errors across the entire path. This "Global Consistency" is what allows a robot to map a 10-kilometer warehouse without the map slowly curving or drifting into nonsense. 2. Fundamentals: The Anatomy of the...