Abstract-High-precision localization of underwater robots in enclosed tunnel environments is challenging due to the absence of satellite signals and the practical difficulty of deploying large-scale acoustic infrastructure. Existing solutions typically fuse inertial navigation with vision, laser, or acoustic sensing, yet their performance is often limited by rapid inertial drift and degraded sensing under turbidity, specular surfaces, and constrained geometries. This paper presents SPLoc, a structural-prior-assisted localization and measurement framework, together with an underwater tunnel pose measurement platform that tightly integrates a blue–green structured-light triangulation sensor and an IMU. The structured-light subsystem acquires dense cross-sectional point clouds with centimeter-scale sampling along both axial and radial directions. From these measurements, tunnel cross-sectional primitives are extracted through feature enhancement, redundancy suppression, and RANSAC-based circle fitting with region-growing verification, and are then parameterized as structural priors. To achieve metrologically consistent fusion, we formulate a least-squares adjustment that explicitly models heading and position states, laser observation residuals, and dominant pose-error terms (including heading/position biases and observation perturbations), and solve it via iterative refinement to provide dynamic pose compensation. Simulation and physical-equivalent experiments on a purpose-built testbed demonstrate that SPLoc achieves < 2 cm localization error and < 0.5° heading error under diverse initial disturbances, while improving robustness against sensing outliers and geometric ambiguities. The proposed platform and estimation model provide a practical measurement route for accurate underwater tunnel robot localization and support engineering deployment where external beacons are unavailable.

Underwater localization is a crucial capability for ensuring robust and accurate vehicle navigation. Although various well-developed localization systems exist, their primary focus is on ground and aerial applications. The challenges posed by underwater environments, such as sparse textures and dynamic disturbances, enable the multi-modal fusion a promising solution for localization. This paper presents AVIP, a localization method that fuses Acoustic, Visual, Inertial, and Pressure modalities for underwater applications. To integrate the information from all modalities during initialization, visual and inertial modalities are alternately assigned as centric sensors to pairwise predict and update estimations of other modalities. The multi-centric calibration problem is addressed through factor graph optimization, which is fully integrated into the graph-based AVIP system as the calibration factor. To evaluate the performance and compare to state-of-the-art approaches, the proposed method is evaluated using semi-physical datasets recorded by a BlueROV2 robot and public real-world datasets. Extensive experiments demonstrate that AVIP achieves superior localization accuracy and exhibits adaptability across a range of sensor configurations.

Reliable localization is essential for the efficiency and safety of autonomous underwater operations. In this study, we propose a novel localization framework centered on a strapdown inertial navigation system, which receives high-precision pose corrections from image-based visible-light positioning. A structured LED array is deployed as a stable underwater positioning reference, and an adaptive optical signal processing method is developed to enhance the extraction of light spot features under challenging scenarios. To address the distortions introduced by cross-medium refraction, we introduce a compensation model that restores accurate camera pose estimation without the need for cumbersome recalibration. Extensive experimental evaluations demonstrate that the proposed system achieves millimeter-level accuracy under favorable scenarios and maintains centimeter-level robustness in unfavorable observation scenarios. Owing to its high-precision, robustness, and cost-effectiveness, the proposed approach holds significant promise for advancing autonomous underwater navigation and next-generation intelligent robotic systems.

The accurate classification of remote sensing (RS) images can quickly identify various geographical features, which is important for planning, utilizing, and protecting natural resources. Recently, the visual Mamba model, as an extension of the vision transformer (ViT), is attracting widespread attention due to its global receptive field and linear complexity. However, the self-attention mechanism of visual transformers can lead to feature collapse in the deep layers, resulting in the disappearance of low-level visual features. In RS images, low-level features, and especially luminance gradient features, can help discern object boundaries and contour information. This is beneficial for the accurate classification of images but has not been fully leveraged. To make full use of contour information and explore the impact of using handcrafted low-level features on the deep layers of the model, in this study, a contour-enhanced Mamba model based on Vision Mamba (VMamba), is proposed, named G-VMamba. The core novelty of G-VMamba lies in its contour enhancement module (ConEM). First, two separate paths are used to extract adaptive luminance gradients and multidimensional convolutional features at each network layer. Subsequently, the features are combined to impose the constraints of low-level features onto the deeper networks. RS image classification experiments were conducted to evaluate the model’s performance, and the results demonstrate the superior performance of G-VMamba in classification tasks. An analysis of class activation maps (CAMs) across different categories shows that G-VMamba focuses more on color (or luminance) change significantly regions in images than models like VMamba, highlighting the efficacy of contour enhancement. The code will be available at:https://github.com/yanliyue/Contour-enhanced-Visual-State-Space-Model.

Localization information is increasingly crucial for incorporating location context into Internet of Things (IoT) data. As an important task in visual localization, keyframe selection helps effective augmentation of visual odometry. Although considerable progress has been made in the research field of keyframe selection, they have rarely focused on dealing with degraded input sensory data in the real world. To this extent, this work proposes a novel concept by incorporating image quality evaluation into the visual localization so that the keyframe selection module can identify images that may cause undesirable effects and take measures to avoid the catastrophic impact of degraded images. The quality for each image is estimated online using deep classifier trained with the image-itself, image-differential, and external information. Since no model-specific knowledge is needed, our method is applicable to any visual localization system. By creating a challenging dataset based on current public datasets under autonomous driving and unmanned aerial vehicles (UAV) scenarios and using it to evaluate our method, we obtain estimated trajectories that are closer to the original situation while validating its robustness to challenging degraded environments.

Thermal images capture temperature information of the environments instead of texture, making it well suitable for obtaining position in dark environments. Many methods have been proposed to handle RGB images, while thermal image-based localization methods are not well studied. To address it, we propose DarkLoc+, a thermal image-based indoor localization method based on the attention model and relative constraints between images under a learning-based localization framework. To be specific, we utilize self-attention to extract reprehensive features from thermal images and exploit relative constraints to enforce the convolutional neural networks (CNNs) to predict global poses. Relative pose loss(RelLoss)and relative regression loss are designed to work with global poses to constrain the network in feature and pose space simultaneously. We evaluate the proposed method on the public thermal images indoor dataset and our own dataset. The experimental results demonstrate that our method can obtain accurate position information.

Nighttime light (NTL) radiance can reflect human settlements and activities. A lot of studies indicate that NTL brightness can be a sound proxy of socioeconomic factors on large scales, such as the population size, gross domestic product, electric power consumption, etc. However, few studies have been dedicated to these topics on fine scales. In this study, we examined the correlation between the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (S-NPP/VIIRS) NTL intensity (NLI) and population density, as well that between NLI and per capita income at two census units’ levels, census tract and block group, in two research areas. The result shows that the NLI has a moderate or weak positive correlation with the population density at both scales. However, when land use type is integrated with population density, the correlation becomes very strong. The NLI and per capita income have a weak or very weak negative correlation at both scales. Moreover, we find that the correlation coefficient is positively correlated with the unit’s size. The larger scale also has a higher correlation coefficient. The research conducted here could be beneficial for the application of S-NPP/VIIRS NTL data in studying socioeconomic activities in human settlement areas.

Cracks in tunnel linings are the most common tunnel defects. As early indicators of structural deterioration, cracks represent critical problems for the safety of tunnels. Several mobile tunnel inspection systems (MTISs) have been developed for tunnel crack inspection. However, due to the weak signals of cracks, these MTISs require considerable exposure time to capture high-quality tunnel images, necessitating a low travel speed. Meanwhile, traditional crack detection methods encounter difficulties in processing tunnel crack images because of their low contrast and poor continuity. To overcome these challenges, this study presents a new MTIS for fast tunnel crack inspection that consists of a novel mobile imaging module and an automatic crack detection module. The imaging module is composed of an array of high-resolution charge-coupled device (CCD) cameras, a mobile laser scanner, and a lighting array. The core of the crack detection module is a novel lightweight convolutional neural network (CNN) designed for efficient tunnel crack detection, with an effective spatial constraint strategy to guarantee crack continuity. We collected a new tunnel crack dataset consisting of 1,218 images using our mobile imaging module at a driving speed of 80 km/h. Comprehensive experiments were conducted on this dataset to evaluate the performance of our proposed network. The results demonstrate that the presented CNN can effectively detect tunnel cracks with state-of-the-art performance, achieving an F1-score greater than 0.88 and an inference speed of 17 FPS with only 3.4M model parameters. The code and data are available athttps://github.com/urban-informatics/LinkCrack.

Accurate built-up area (BA) information plays crucial role for many applications. PolSAR imagery can provide important source for BAs information analysis. However, the BAs with large orientation angles are usually misdetected as vegetation, and labeled BA samples with special orientations are diffi-cult to obtain. In this paper, a PolSAR BA extraction method based on eigenvalue statistical information and PU-Learning is proposed to overcome abovementioned problems. Firstly, the roll invariance of coherency-matrix eigenvalues and the building orientations have been analyzed. Then, by adopting eigenvalue-Wishart unsupervised classification, regional statistical information and rotation invariant property are comprehensively utilized. Finally, the BAs are extracted by combining PU-Learning classifier with only positive samples at same distinguishable orientation. Six experiments on PolSAR imageries show the accuracy of proposed method can reach 92-99% with only a few positive samples, 8-20% higher than classical model decomposition-based PU-Learning method, and the requirement for labeled samples is less than 0.65%.