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This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

This study employed a questionnaire survey to understand the safety attitudes, focusing on safety motivation and risk tolerance as well as safety management practices, including safety training and safety incentives, among construction site employees, including both managerial personnel and frontline workers. The objective was to explore the relationship between safety management practices and safety attitudes within both the managerial and frontline worker groups.

This study was conducted among 1,026 construction workers and 256 managerial personnel at 53 construction sites across 12 provinces in China. A t-test was used to compare the differences in safety-related scores between managerial personnel and frontline workers, and a structural equation model was used to explore the relationship between safety attitudes and safety management.

This study found that the scores of managerial personnel for safety motivation, safety training and safety incentives were significantly higher than construction workers, while their scores for risk tolerance were significantly lower than construction workers. Managerial personnel’s safety motivation has a significant positive impact on both safety training and safety incentives, while their risk tolerance has a significant negative impact on safety incentives. Safety training has a significant positive effect on construction workers’ safety motivation, whereas safety incentives have a significant negative impact on construction workers’ risk tolerance.

This study is one of the few that have investigated construction safety by conducting surveys targeting both site managerial personnel and frontline workers, employing an empirical approach to validate the role of safety management in transmitting safety attitudes from site managerial personnel to frontline workers.