IL-SLAM: Intelligent Line-assisted SLAM Based on Feature Awareness for Dynamic Environments

2025-09-02

AI Summary

Traditional SLAM systems suffer from tracking degradation in dynamic environments due to aggressive removal of dynamic point features, while existing methods that indiscriminately incorporate line or plane features incur redundant computation and noise accumulation. To address this, we propose a feature-aware intelligent line-feature assistance mechanism. It dynamically activates line features for local tracking and mapping only when point-feature quality or motion consistency falls below a threshold, while explicitly excluding them from global optimization to prevent error propagation. Our framework integrates ORB point features, robust line tracking, geometry-constrained local map construction, and quality-weighted loop closure detection. Evaluated on the TUM dynamic sequences, our method significantly outperforms baselines including ORB-SLAM3, achieving average reductions of 32.7% in absolute trajectory error (ATE) and 28.4% in relative pose error (RPE), thereby demonstrating an effective balance among accuracy, robustness, and computational efficiency.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Machine Learning: Feature Construction/Reformulation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Visual Simultaneous Localization and Mapping (SLAM) plays a crucial role in autonomous systems. Traditional SLAM methods, based on static environment assumptions, struggle to handle complex dynamic environments. Recent dynamic SLAM systems employ geometric constraints and deep learning to remove dynamic features, yet this creates a new challenge: insufficient remaining point features for subsequent SLAM processes. Existing solutions address this by continuously introducing additional line and plane features to supplement point features, achieving robust tracking and pose estimation. However, current methods continuously introduce additional features regardless of necessity, causing two problems: unnecessary computational overhead and potential performance degradation from accumulated low-quality additional features and noise. To address these issues, this paper proposes a feature-aware mechanism that evaluates whether current features are adequate to determine if line feature support should be activated. This decision mechanism enables the system to introduce line features only when necessary, significantly reducing computational complexity of additional features while minimizing the introduction of low-quality features and noise. In subsequent processing, the introduced line features assist in obtaining better initial camera poses through tracking, local mapping, and loop closure, but are excluded from global optimization to avoid potential negative impacts from low-quality additional features in long-term process. Extensive experiments on TUM datasets demonstrate substantial improvements in both ATE and RPE metrics compared to ORB-SLAM3 baseline and superior performance over other dynamic SLAM and multi-feature methods.

Problem

• Dynamic SLAM struggles with insufficient point features after removing dynamic objects
• Continuous introduction of unnecessary line features increases computational overhead and noise
• Need intelligent mechanism to activate line features only when necessary

Innovation

• Feature-aware mechanism activates line features only when necessary
• Line features assist tracking but excluded from global optimization
• Reduces computational overhead and minimizes low-quality feature introduction