Dual-Mode SPL-SLAM: A Robust Semantic Point-Line SLAM System With Monocular Neural Depth and Sensor Fusion for Intelligent Transportation

Abstract

Simultaneous Localization and Mapping (SLAM) is critical for Intelligent Transportation Systems (ITS), yet existing solutions struggle to balance accuracy, sensor versatility, and computational efficiency. Traditional monocular methods suffer from scale drift, while RGB-D systems with heavy semantic networks often exceed the computational budgets of onboard processors. To address these conflicting constraints, we propose Dual-Mode SPL-SLAM, a unified adaptive framework capable of adapting to different intelligent driving agents. The system intelligently switches between two operating paths: Mode I (Sensor-Semantic) leverages physical depth sensors and high-precision semantic segmentation for complex dynamic environments. To further improve accuracy, we use line-feature processing and a refined epipolar-constraint error for robust detection of known and unknown dynamic objects; Mode II (Neural-Clustering) employs a lightweight neural network (LiteMono) and K-means++ clustering within the object bounding box predicted by GPU-accelerated YOLOX to recover scale and filter dynamic objects using fast LK optical flow in monocular setups. Crucially, both modes converge into a shared Adaptive Point-Line Backend, which dynamically weights features based on environmental texture to ensure robustness. Extensive experiments on KITTI dataset demonstrate that our system achieves state-of-the-art accuracy and real-time performance across diverse sensor configurations.