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Localization based on single-line lidar is widely used in various robotics applications, such as warehousing, service, transit, and construction, due to its high accuracy, cost-effectiveness, and minimal computational requirements. However, challenges such as LiDAR degeneration and frequent map changes persist in hindering its broader adoption. To address these challenges, we introduce the Contribution Sampling and Map-Updating Localization (CSMUL) algorithm, which incorporates weighted contribution sampling and dynamic map-updating methods for robustness enhancement. The weighted contribution sampling method assigns weights to each map point based on the constraints within degenerate environments, significantly improving localization robustness under such conditions. Concurrently, the algorithm detects and updates anomalies in the map in real time, addressing issues related to localization drift and failure when the map changes. The experimental results from real-world deployments demonstrate that our CSMUL algorithm achieves enhanced robustness and superior accuracy in both degenerate scenarios and dynamic map conditions. Additionally, it facilitates real-time map adjustments and ensures continuous positioning, catering to the needs of dynamic environments. [ABSTRACT FROM AUTHOR]

Localization based on single-line lidar is widely used in various robotics applications, such as warehousing, service, transit, and construction, due to its high accuracy, cost-effectiveness, and minimal computational requirements. However, challenges such as LiDAR degeneration and frequent map changes persist in hindering its broader adoption. To address these challenges, we introduce the Contribution Sampling and Map-Updating Localization (CSMUL) algorithm, which incorporates weighted contribution sampling and dynamic map-updating methods for robustness enhancement. The weighted contribution sampling method assigns weights to each map point based on the constraints within degenerate environments, significantly improving localization robustness under such conditions. Concurrently, the algorithm detects and updates anomalies in the map in real time, addressing issues related to localization drift and failure when the map changes. The experimental results from real-world deployments demonstrate that our CSMUL algorithm achieves enhanced robustness and superior accuracy in both degenerate scenarios and dynamic map conditions. Additionally, it facilitates real-time map adjustments and ensures continuous positioning, catering to the needs of dynamic environments.

An analysis of the soil cover of Russia as presented on the soil map on a scale of 1 : 2.5 M with the use of a new substantive-genetic soil classification system has been performed at the level of soil orders. The high level of classification-based generalization makes it possible to assess the most general patterns of soil geography and soil resources and to identify changes that have occurred as a result of renaming of each polygon on the map with the use of the new classification. The areas occupied by soil orders have been calculated. In total, there are 24 soil orders on the new map, including 21 orders of natural soils and 3 orders (agrozems, turfzems, stratozems) of anthropogenically transformed soils. Soils of the orders of agro-abrazems, chernozems, and turbozems are not presented on the map. As on most small-scale soil maps of Russia, the zonal regularities of the soil cover in the East European Plain and high lithogenic mosaicity in Central and Eastern Siberia are clearly seen. The new map includes soil orders that were absent on the initial map: cryozems, cryometamorphic and hydrometamorphic soils, lithozems, cryoabrazems, cryoturbozems, urbostratozems, and organo-accumulative soils. Soils characteristic of humid conditions predominate: Al-Fe-humus soils (Podzols) (319.2 M ha, or 19% of the land fund of Russia), gley soils (Gleysols) (223.9 M ha, 13%), texture-differentiated soils (Luvisols and Regosols) (190.8 M ha, 11%), and peat soils (Histosols) (143.5 M ha, 8%) and occupy more than a half of the territory of Russia. The area of humus-accumulative soils most suitable for arable use is 103.6 M ha (6%). Considerable areas are occupied by soils of the orders of cryozems (Turbic Cryosols) (111.4 M ha), iron-metamorphic soils (Chromic Cambisols) (92.7 M ha), structure-metamorphic soils (Cambisols) (47.3 M ha), pale-metamorphic soils (Cambic Cryosols) (12.8 M ha), hydrometamorphic soils (Calcic Gleysols) (4.3 M ha), and cryometamorphic soils (Cambisols Gelic) (3.4 M ha), which corresponds to the vast continental territory of Russia with balanced moisture conditions. Separate place belongs to the soils with strict limitations for use (lithosols (Leptosols), weakly developed soils (Regosols, Nudilithic Leptosols)) but playing important biospheric functions and requiring special protection.

High-definition digital map is the foundation of autonomous driving technology, which provides precise location information for autonomous vehicles. However, with the change of environment and the passage of time, the fidelity performance of unstructured road map will gradually decline. Therefore, this paper proposes a digital map updating method of open-pit mine based on vehicle-cloud collaboration to effectively improve the efficiency of map updating. Firstly, a three-layer map model structure is constructed to ensure the integrity of map updating of open-pit mine. Then, a track-based map updating method is proposed for many scenarios of open-pit mines. Meanwhile, a map updating framework based on vehicle-cloud collaboration is designed to improve the efficiency of automatic driving map updating in open-pit mines. Experimental results show that this method can effectively reduce the map updating delay. Compared with the single-vehicle mode, the average map updating delay is reduced by 75%. At the same time, the relative error of map updating is reduced, and the overall accuracy is increased by 10%, which meets the requirements of map fidelity.