Although laser sensors offer high-precision localization capabilities, they tend to be bulky, costly, and energy-intensive, making them inconvenient for rescuers to carry and use in complex rescue environments [30; 31]. Therefore, infrastructure-based navigation methods are difficult to apply effectively in underground disaster environments, failing to meet the demands of high-efficiency and reliable localization for emergency rescue, underground tunnel inspections, and exploration of unknown areas. In recent years, pedestrian autonomous localization systems that operate without relying on infrastructure have attracted considerable attention [32; 33]. The primary challenge for such systems lies in maintaining high precision during long-distance tasks. As a result, visual navigation and inertial navigation have become central areas of focus within the field of autonomous navigation research. However, inertial navigation systems suffer from error accumulation over time, while visual navigation can experience localization divergence in environments with weak textures. To address these challenges, this paper proposes an autonomous navigation method tailored for complex spaces, leveraging APDR and visual localization. Specifically, the proposed method integrates advanced APDR and enhanced visual localization method to mitigate the issues of localization divergence in complex environments and error accumulation in inertial sensors. It exhibits strong adaptability and high localization accuracy, capable of meeting the localization needs of rescuers in complex environments. Our contributions are summarized as follows:  
1. To improve positioning accuracy, we collected multi-source sensor data from various body parts of rescue personnel under different motion states. A data feature discriminability model was constructed to analyze the differences among multiple characteristic parameters. Based on the analysis results, the optimal placement position for the data acquisition unit and the input feature parameters for the adaptive step length estimation model were selected. 2. To address the limitations of traditional adaptive step length estimation methods specifically, poor adaptability to different motion patterns and low estimation accuracy, an improved adaptive step length estimation model is proposed. By analyzing data characteristics and applying multiple constraint conditions, the model achieves adaptive extraction of step length intervals. The step length estimation model was trained using selected input features with significant feature discriminability, enabling accurate estimation across various motion patterns and step lengths. 3. This paper innovatively integrates step length features into the visual localization and tracking algorithm. Based on step length information, the fusion processing is implemented from three aspects: keyframe insertion mechanism, translation component constraint, and feature matching search region constraint. These strategies collectively ensure the accuracy and robustness of the visual tracking system in complex environments. Additionally, a wearable autonomous localization vest system is designed and tested in indoor corridors, underground parking lots, and tunnel environments. The remainder of this paper is organized as follows: Section 2 introduces the overall framework of the proposed localization system. Section 3 details the data analysis and preprocessing. Section 4 introduces the methodology of our methods. Section 5 presents the experiments and results. Finally, the conclusion is given in Section 6.