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Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrate that the proposed method achieves accurate trajectory estimation and consistent mapping.

Background Understanding the mapping precision of genome-wide association studies (GWAS), that is the physical distances between the top associated single-nucleotide polymorphisms (SNPs) and the causal variants, is essential to design fine-mapping experiments for complex traits and diseases. Results Using simulations based on whole-genome sequencing (WGS) data from 3642 unrelated individuals of European descent, we show that the association signals at rare causal variants (minor allele frequency ≤ 0.01) are very unlikely to be mapped to common variants in GWAS using either WGS data or imputed data and vice versa. We predict that at least 80% of the common variants identified from published GWAS using imputed data are within 33.5 Kbp of the causal variants, a resolution that is comparable with that using WGS data. Mapping precision at these loci will improve with increasing sample sizes of GWAS in the future. For rare variants, the mapping precision of GWAS using WGS data is extremely high, suggesting WGS is an efficient strategy to detect and fine-map rare variants simultaneously. We further assess the mapping precision by linkage disequilibrium between GWAS hits and causal variants and develop an online tool (gwasMP) to query our results with different thresholds of physical distance and/or linkage disequilibrium ( http://cnsgenomics.com/shiny/gwasMP ). Conclusions Our findings provide a benchmark to inform future design and development of fine-mapping experiments and technologies to pinpoint the causal variants at GWAS loci.

Driven by the concepts of digital twin and metaverse, constructing a high-fidelity, semantic-rich, and interactive digital copy of the physical world has become a key issue in the field of surveying, mapping, and geographic information. However, in typical complex landforms such as urban canyons and mountainous forest areas, the single-sensor data acquisition methods (such as UAV oblique images and lidar) has inherent information blind spots and accuracy bottlenecks. Traditional data fusion approaches predominantly focus on shallow geometric alignment and splicing at the geometric level, ignoring the heterogeneity of different data sources in semantic connotations, leads to common problems such as geometric distortion, detail loss, and semantic inconsistency in the fusion products. To break through this dilemma, this paper proposes an adaptive fusion framework for multi-source data of complex landforms (SAAF-Net) with deeply coupled semantic information. Centered on computer vision, this framework constructs a full-link technical process from raw data to high-precision semantic 3D models: Two-stream parallel semantic parsing: A two-stream deep semantic segmentation network for images and point clouds (based on SegFormer and PointNeXt) is designed to achieve fine-grained classification of scene features (the average intersection over union mIoU exceeds 90%), providing high-dimensional semantic priors for fusion. Semantic-guided cross-source registration: A semantic weighted iterative closest point algorithm (SW-ICP) is proposed. By constraining the corresponding point search space through cross-source semantic consistency and combining with the significance weighting of local geometric structures, the robustness problem of heterogeneous data registration is solved. Neural adaptive fusion modeling: A multi-factor driven neural network model is constructed to dynamically evaluate the confidence of data sources under different semantic categories and observation conditions, achieving the optimal fusion of pixel-level elevation and texture. Experiments in the city center and mountainous forest areas show that compared with mainstream methods, the root mean square error (RMSE) of SAAF-Net is reduced by 35% − 48%, and the completeness is improved to over 99%. Especially, the reconstruction quality in building edges, vegetation-covered areas, and light-shadow areas is significantly improved.with a substantial enhancement in visual realism. This research provides theoretical and technical support for the construction of a high-precision 3D base for digital twin cities.

It is great challenge for visually impaired (VI) people to shop in narrow and crowded farmers’ markets. However, there is no research related to guiding them in farmers’ markets worldwide. This paper proposes the Radio-Frequency–Visual Tag Positioning and Automatic Detection (RFTPAD) algorithm to quickly build a high-precision navigation map. It combines the advantages of visual beacons and radio-frequency signal beacons to accurately calculate the guide robot’s coordinates to correct its positioning error and simultaneously perform the task of mapping and detecting information. Furthermore, this paper proposes the A*-Fixed-Route Navigation (A*-FRN) algorithm, which controls the robot to navigate along fixed routes and prevents it from making frequent detours in crowded aisles. Finally, this study equips the guide robot with a flexible robotic arm and proposes the Intelligent-Robotic-Arm-Guided Shopping (IRAGS) algorithm to guide VI people to quickly select fresh products or guide merchants to pack and weigh products. Multiple experiments conducted in a 1600 m2 market demonstrate that compared with the classic mapping method, the accuracy of RFTPAD is improved by 23.9%. What is more, compared with the general navigation method, the driving trajectory length of A*-FRN is 23.3% less. Furthermore, the efficiency of guiding VI people to select products by a robotic arm is 100% higher than that through a finger to search and touch.

Background The identification and mapping of at-risk populations at a lower administrative level than the district are prerequisites for the planning, resource allocation and design of impactful control intervention measures. Thus, the objective of the current study was to conduct sub-district precision mapping of soil-transmitted helminthiasis (STH) and schistosomiasis in 29 districts of north-western Tanzania using the current recommended World Health Organization criteria. Methods A cross-sectional survey was conducted in 145 schools between March and May 2021. A urine filtration technique was used for the quantification of Schistosoma haematobium eggs, whereas quantification of Schistosoma mansoni and STH eggs was done using the Kato–Katz technique. Microhaematuria was examined using a urine dipstick. Results The overall prevalences of any STH and schistosome infections were 9.3% [95% confidence interval (95%CI) 8.6–9.9] and 14.6% (95%CI 13.9–15.4), respectively. The overall prevalence of S. mansoni was 8.7% (95%CI 8.1–9.3), and 36.4%, 41.6%, and 21.9% of the children had low, moderate, and heavy infections, respectively. The overall prevalence of S. haematobium was 6.1% (95%CI 5.5–6.5), and 71.7% and 28.3% of the infected children had light and heavy intensity infections, respectively. The prevalence of microhaematuria was 7.3% (95%CI 6.7–7.8), with males having the highest prevalence (8.4%, P  < 0.001). The prevalences of Trichuris trichiura , Ascaris lumbricoides and hookworm were, respectively, 1.3% (95%CI 0.1–1.5), 2.9% (95%CI 2.5–3.3) and 6.2% (95%CI 5.7–6.7). Most of the children infected with STH had light to moderate intensities of infection. The overall prevalence of co-infection with STH and schistosomiasis was 19.1%. The prevalence of schistosomiasis ( P  < 00.1) and STH ( P  < 0.001) varied significantly between schools and sub-districts. Schistosoma mansoni and S. haematobium were observed in 60 and 71 schools, respectively, whereas any STH was observed in 49 schools. In schools where schistosomiasis was observed, prevalence was < 10% in 90.8% of them, and ranged from ≥ 10% to < 50% in the other 9.2%. In schools where any STH was observed, the prevalence was < 10% in 87.7% of them. Conclusions The data reported here show that schistosomiasis and STH are widely distributed around Lake Victoria. In most of the schools where schistosomiasis and STH occurred the transmission thresholds were low. These data are important and need to be taken into consideration when decisions are made on the implementation of the next round of mass chemotherapies for schistosomiasis and STH in Tanzania. Graphical Abstract

The accuracy and stability of front-end point cloud registration algorithms are crucial for the mapping and localization precision in laser SLAM (simultaneous localization and mapping) systems. Traditional point-to-line and point-to-plane iterative closest point (ICP) registration algorithms, widely used in SLAM front ends, often suffer from low efficiency, significant data dependency during the matching process, and a propensity for local optima. This registration method exhibits a more pronounced local optimum issue in large-scale SLAM mapping, thereby diminishing matching accuracy and increasing reliance on initial values. To address these limitations, this paper introduces NI-LIO, a novel SLAM algorithm that integrates ICP with normal distributions transform (NDT) to enhance localization accuracy, computational efficiency and robustness. By combining the precision of ICP with the robustness of NDT, the proposed algorithm significantly improves system stability and localization accuracy. The analysis of mapping and localization experiments indicates a significant reduction in errors compared to traditional SLAM algorithms, with experiments showing a REMS value decrease of over 20%. Compared to ALOAM, FAST_LIO2 and Lego-LOAM algorithms, the new NI-LIO algorithm shows improvements in both accuracy and stability, enabling the construction of a more precise and consistent global map. This algorithm exhibits excellent adaptability to various environments.

Soil acidification is a major and growing concern in many cropping regions globally. Whilst spatial variability in acidification is a common consideration in the management of soil health and fertility at sub-paddock scale, insufficient focus has been directed toward the identification of this variability. Suitability of portable visible near infrared reflectance (vis–NIR) spectroscopy was assessed in this study as a potential technique to achieve rapid, precise, inexpensive and spatially specific quantification of key soil parameters to inform lime requirements. Spectral fingerprints were taken using a 1 ha grid sampling approach, with four sampling protocols investigated as follows, scans: (i) directly on cleared soil surfaces; (ii) on 0–100 mm undisturbed cores; (iii) on dried 0–100 mm cores; and finally (iv) on dried, ground, sieved and mixed cores. Data was analysed using a partial least squares regression (PLSR) model to identify the strength of linear relationship between reference chemistry data and predictions derived from spectral readings. Lime requirement maps using vis–NIR predictions were then theoretically compared against traditional aggregated sampling patterns while considering the trade-offs between accuracy, economics and agronomy associated with the identification of spatial variability. The vis–NIR measurements demonstrated moderate predictive capabilities in field for determining pH (R2 = 0.3–0.5) and liming requirements (R2 = 0.5–0.6) rapidly at high spatial resolution. Vis–NIR in field mapping techniques, which enable the use of site specific management of soil resources, were found to positively redirect lime resources from alkaline areas toward acidic areas of the paddock, resulting in minimal difference to overall expenditure on lime purchase and potential for increased agronomic benefits over the long-term. Further spectral library development, calibration, and research on in-field sampling methods is recommended.

Precise mapping and tracking of wheat crops are crucial to improve agricultural management, particularly for small farms in challenging landscapes such as Nepal. By utilizing temporal Sentinel-2 imagery, this research maps wheat fields by examining phenological stages using machine learning methods, which enhances classification accuracy. Sentinel-2, a component of the Copernicus program by the European Space Agency, offers high-quality multispectral images for precise monitoring of crop growth over time. Two classification models, Random Forest (RF) and Support Vector Machine (SVM), were employed to distinguish wheat from non-wheat areas. The accuracy of classification was improved by integrating in-situ data collected with Kobo Toolbox. The findings showed that Random Forest performed better than SVM, reaching 99% accuracy in training and 86% in validation, with 56%of the study region classified as wheat. RF's outstanding performance is due to its capacity to manage temporal and spectral intricacies, particularly in capturing the phenological cycle of crops. This research showcases how machine learning, specifically Random Forest, can enhance the accuracy of wheat mapping for small farms by analyzing phenological stages effectively, with plans to apply these methods to rice and maize in the future.

Photogrammetric processing is available in various software solutions and can easily deliver 3D pointclouds as accurate as 1 pixel. Certain applications, e.g., very accurate shape reconstruction in industrial metrology or change detection for deformation studies in geosciences, require results of enhanced accuracy. The tie-point extraction step is the opening in the photogrammetric processing chain and therefore plays a key role in the quality of the subsequent image orientation, camera calibration and 3D reconstruction. Improving its precision will have an impact on the obtained 3D. In this research work we describe a method which aims at enhancing the accuracy of image orientation by adding a second iteration photogrammetric processing. The result from the classical processing is used as a priori information to guide the extraction of refined tie-points of better photogrammetric quality. Evaluated on indoor and UAV acquisitions, the proposed methodology shows a significant improvement on the obtained 3D point accuracy.

Background Fine-scale mapping of schistosomiasis to guide micro-targeting of interventions will gain importance in elimination settings, where the heterogeneity of transmission is often pronounced. Novel mobile applications offer new opportunities for disease mapping. We provide a practical introduction and documentation of the strengths and shortcomings of GPS-based household identification and participant recruitment using tablet-based applications for fine-scale schistosomiasis mapping at sub-district level in a remote area in Pemba, Tanzania. Methods A community-based household survey for urogenital schistosomiasis assessment was conducted from November 2020 until February 2021 in 20 small administrative areas in Pemba. For the survey, 1400 housing structures were prospectively and randomly selected from shapefile data. To identify pre-selected structures and collect survey-related data, field enumerators searched for the houses’ geolocation using the mobile applications Open Data Kit (ODK) and MAPS.ME. The number of inhabited and uninhabited structures, the median distance between the pre-selected and recorded locations, and the dropout rates due to non-participation or non-submission of urine samples of sufficient volume for schistosomiasis testing was assessed. Results Among the 1400 randomly selected housing structures, 1396 (99.7%) were identified by the enumerators. The median distance between the pre-selected and recorded structures was 5.4 m. A total of 1098 (78.7%) were residential houses. Among them, 99 (9.0%) were dropped due to continuous absence of residents and 40 (3.6%) households refused to participate. In 797 (83.1%) among the 959 participating households, all eligible household members or all but one provided a urine sample of sufficient volume. Conclusions The fine-scale mapping approach using a combination of ODK and an offline navigation application installed on tablet computers allows a very precise identification of housing structures. Dropouts due to non-residential housing structures, absence, non-participation and lack of urine need to be considered in survey designs. Our findings can guide the planning and implementation of future household-based mapping or longitudinal surveys and thus support micro-targeting and follow-up of interventions for schistosomiasis control and elimination in remote areas. Trial registration ISRCTN, ISCRCTN91431493. Registered 11 February 2020, https://www.isrctn.com/ISRCTN91431493