Efficient Incremental SLAM via Information-Guided Gating and Selective Partial Optimization

We present an efficient incremental SLAM back-end that reduces computation while preserving accuracy close to that of a full incremental Gauss–Newton (GN) solver across benchmark pose-graph datasets. The method combines information-guided gating (IGG), which uses a log-determinant-based information surrogate to decide when broad updates are warranted, with selective partial optimization (SPO), which confines multi-iteration GN updates to variables that remain affected after each iteration. We provide a local perturbation analysis, showing that, under standard regularity conditions, the proposed approximation tracks full GN within a threshold-controlled neighborhood and recovers the same local minimizer and asymptotic convergence rate when the effective approximation error vanishes asymptotically. Experiments on benchmark pose-graph SLAM datasets show competitive final and increment-averaged accuracy together with substantial reductions in update and solve FLOPs. These results support IGG-SPO as a practically promising SLAM back-end for robots operating under limited onboard computational resources.