Efficient Incremental SLAM via Information-Guided and Selective Optimization

2026-01-13

AI Summary

This work proposes an efficient back-end optimization framework for incremental SLAM that reconciles the trade-off between global accuracy and computational efficiency. By integrating Information-Guided Gating (IGG) with Selective Partial Optimization (SPO), the method iteratively updates only the subset of variables most affected by new observations while preserving all measurements to maintain global consistency. Global optimization is dynamically triggered based on the log-determinant of the information matrix, and a multi-iteration Gauss-Newton strategy is employed to ensure convergence. Experiments on standard SLAM benchmarks demonstrate that the approach achieves accuracy comparable to batch solvers while significantly outperforming conventional incremental methods in computational efficiency, making it well-suited for real-time, data-intensive applications.

Technology Category

• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We present an efficient incremental SLAM back-end that achieves the accuracy of full batch optimization while substantially reducing computational cost. The proposed approach combines two complementary ideas: information-guided gating (IGG) and selective partial optimization (SPO). IGG employs an information-theoretic criterion based on the log-determinant of the information matrix to quantify the contribution of new measurements, triggering global optimization only when a significant information gain is observed. This avoids unnecessary relinearization and factorization when incoming data provide little additional information. SPO executes multi-iteration Gauss-Newton (GN) updates but restricts each iteration to the subset of variables most affected by the new measurements, dynamically refining this active set until convergence. Together, these mechanisms retain all measurements to preserve global consistency while focusing computation on parts of the graph where it yields the greatest benefit. We provide theoretical analysis showing that the proposed approach maintains the convergence guarantees of full GN. Extensive experiments on benchmark SLAM datasets show that our approach consistently matches the estimation accuracy of batch solvers, while achieving significant computational savings compared to conventional incremental approaches. The results indicate that the proposed approach offers a principled balance between accuracy and efficiency, making it a robust and scalable solution for real-time operation in dynamic data-rich environments.

Problem

• incremental SLAM
• computational efficiency
• global consistency
• real-time operation
• optimization

Innovation

• Information-Guided Gating
• Selective Partial Optimization
• Incremental SLAM
• Efficient Optimization
• Information-Theoretic Criterion