Most existing deep learning-based camera pose estimation methods rely solely on visible images, making them vulnerable to challenges such as drastic lighting variations and poor nighttime imaging quality. These limitations reduce the robustness and accuracy of traditional approaches. To address these issues, we propose VIFPE (Visible- and Infrared-Fusion-Based Pose Estimation), a novel framework that integrates features from both visible and infrared images to enhance pose estimation in urban environments. By leveraging the complementary advantages of visible images, which are rich in texture, and infrared images, which are resilient to illumination changes, VIFPE achieves robust and accurate localization across diverse lighting conditions. The framework consists of multiple key modules, including a feature extractor, a multimodal fusion module, a pose regressor, and a multi-task learning strategy. Specifically, VIFPE employs Multi-axis Vision Transformer (MaxViT) encoders to extract features from both modalities, which are then fused in a shared feature space through a cross-modal fusion module. A pose regression network is subsequently trained to estimate the camera’s position and orientation based on the fused representation. Our experimental results on diverse urban datasets demonstrate that VIFPE significantly outperforms conventional methods in terms of both accuracy and robustness, particularly under challenging lighting conditions. This work underscores the potential of multimodal fusion for camera localization in large-scale urban environments and lays a foundation for future advancements in the field.

Large-scale camera localization in driving environments is pivotal in autonomous driving and robotics, where deep learning-based solutions offer promise for end-to-end problem-solving. However, existing learning-based camera pose regression methods often encounter accuracy and robustness challenges in complex environments, hampering scalability to large scenarios and datasets. This work introduces EMaxLoc, a novel end-to-end camera pose regression model that integrates a Multi-axis Vision Transformer (MaxViT) with an Efficient Multi-scale Attention (EMA) module for large-scale localization. The introduced MaxViT extractor captures both local and global spatial features through its block and grid attention mechanisms, while the EMA module enhances multi-scale feature fusion and channel-wise contextual modeling. EMaxLoc demonstrates remarkable proficiency in environments characterized by scene dynamics and illumination changes. Furthermore, our Transformer-based framework showcases scalability, suggesting potential for continual improvement in localization performance. Extensive experiments validate EMaxLoc’s superior performance with increasing dataset sizes, surpassing existing deep learning-based methods such as AtLoc, PoseNet, MapNet and EffLoc in large-scale driving environments.

Dual-foot pedestrian navigation systems leveraging zero-velocity updates (ZUPTs) can effectively reduce heading drift by incorporating inter-foot ranging constraints. However, their performance is highly sensitive to ranging noise and zero-velocity detector accuracy–misdetections introduce outlier observations that degrade state estimation. Conventional robust kernel-based outlier suppression mitigates this issue but introduces strong non-convexities, making the optimization prone to convergence at local minima. To address these challenges, we propose a dual-foot Fpedestrian inertial navigation algorithm based on factor graph optimization (FGO) with graduated non-convexity (GNC). The system fuses foot-mounted IMU measurements, zero-velocity constraints, and inter-foot ranging information within a sliding-window factor graph. A GNC-based backend adaptively reweights factor contributions, suppressing the effect of outliers while progressively reducing the non-convexity introduced by robust kernels. Extensive experiments demonstrate that the proposed method enhances robustness and positioning accuracy across diverse and complex pedestrian motion conditions, outperforming conventional EKF-based and standard FGO approaches.