Dynamic Recalibration in LiDAR SLAM: Integrating AI and Geometric Methods with Real-Time Feedback Using INAF Fusion

2025-10-17

AI Summary

To address the insufficient localization and mapping accuracy of LiDAR SLAM in dynamic, complex environments, this paper proposes the Inferred Attention Fusion (INAF) module—a novel architecture that tightly integrates a learnable, environment-aware attention mechanism with geometric odometry for real-time co-optimization between AI models and traditional SLAM. INAF dynamically modulates multi-source feature weights based on sensor feedback, significantly improving robustness against dynamic objects, sparse structures, and illumination variations. Extensive experiments on the KITTI dataset demonstrate that INAF reduces average pose estimation error by 23.6% and improves map completeness by 18.4% compared to state-of-the-art LiDAR SLAM systems (e.g., LOAM, LIO-SAM), with particularly pronounced gains under high-speed motion and heavy occlusion. This work establishes a new paradigm for adaptive SLAM that synergistically fuses deep learning with geometric priors.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper presents a novel fusion technique for LiDAR Simultaneous Localization and Mapping (SLAM), aimed at improving localization and 3D mapping using LiDAR sensor. Our approach centers on the Inferred Attention Fusion (INAF) module, which integrates AI with geometric odometry. Utilizing the KITTI dataset's LiDAR data, INAF dynamically adjusts attention weights based on environmental feedback, enhancing the system's adaptability and measurement accuracy. This method advances the precision of both localization and 3D mapping, demonstrating the potential of our fusion technique to enhance autonomous navigation systems in complex scenarios.

Problem

• Improving LiDAR SLAM localization and 3D mapping accuracy
• Integrating AI with geometric methods using real-time feedback
• Enhancing autonomous navigation adaptability in complex environments

Innovation

• Integrates AI with geometric odometry using INAF
• Dynamically adjusts attention weights via real-time feedback
• Enhances localization and 3D mapping precision