This paper presents a tightly integrated fusion algorithm that accelerates Precise Point Positioning (PPP) convergence and enhances localization robustness in complex urban environments. The proposed method combines a filter-based visual–inertial odometry (VIO) framework with semantic constraints from a high-definition (HD) vector map, incorporating lane-line and directional arrow features extracted via a neural network. These HD map observations provide reliable lateral and heading constraints, effectively compensating for the degraded observability and drift in traditional VIO systems. In satellite-visible conditions, they also offer accurate initial estimates that significantly accelerate PPP convergence. The system is evaluated in diverse real-world scenarios—including open roads, urban canyons, tunnels, and soundproofed elevated highways—demonstrating sub-meter positioning accuracy and PPP convergence within 3–10 seconds, representing up to a 98.33% reduction in convergence time compared to conventional PPP. Furthermore, the approach operates using low-cost inertial and visual sensors, ensuring practical deployment in large-scale autonomous navigation systems.

Dense partial-view LiDAR observations are attractive for outdoor perception, but limited overlap and viewpoint sensitivity make odometry and mapping less reliable than with spinning LiDARs. Many recent algorithms for this sensing regime are built as LiDAR-inertial odometry frameworks, whose localization and mapping performance can degrade or fail when the IMU state estimation becomes unstable. This paper presents a LiDAR-only framework for complex outdoor scenes using a factor-graph back-end. After denoising and motion compensation, the point cloud is projected onto a range image for ground, planar, edge, and line extraction. Pose estimation is strengthened by degeneracy-aware feature selection, while loop closing combines scan-based and path-based cues to handle partial-view revisits. Experiments in tunnels, urban roads, residential areas, and other challenging scenes show reduced drift and improved mapping consistency for dense limited-FoV LiDAR data.