In large-scale structured scenes, efficient and reliable global localization is hard to achieve for mobile robots due to the substantial increase in the search space. This article addresses this issue by proposing an optimized branch and bound (BnB) based global localization using multiscale/resolution maps, which can efficiently find the global optimal initial pose with a high success rate. Multiscale feature maps (MSFMs) are first constructed to reflect the explicit point/line information of the structured scene. Then, to narrow the position space, robust features are extracted from LiDAR data at different scales and matched with MSFMs instantaneously using the hash function. Utilizing the attitude and heading reference system, a yaw angle estimation considering environmental interference is achieved to reduce the orientation space and improve the localization reliability in ambiguous environments. Besides, with the optimal resolution level and sparse point cloud sampled from interval filtering, an optimized BnB-based search is presented by using multiresolution maps. Finally, the effectiveness of the proposed method is verified by mobile robot real-world and simulated experiments in large-scale structured environments.

Three-dimensional feature description for a local surface is a core technology in 3D computer vision. Existing descriptors perform poorly in terms of distinctiveness and robustness owing to noise, mesh decimation, clutter, and occlusion in real scenes. In this paper, we propose a 3D local surface descriptor using point-pair transformation feature histograms (PPTFHs) to address these challenges. The generation process of the PPTFH descriptor consists of three steps. First, a simple but efficient strategy is introduced to partition the point-pair sets on the local surface into four subsets. Then, three feature histograms corresponding to each point-pair subset are generated by the point-pair transformation features, which are computed using the proposed Darboux frame. Finally, all the feature histograms of the four subsets are concatenated into a vector to generate the overall PPTFH descriptor. The performance of the PPTFH descriptor is evaluated on several popular benchmark datasets, and the results demonstrate that the PPTFH descriptor achieves superior performance in terms of descriptiveness and robustness compared with state-of-the-art algorithms. The benefits of the PPTFH descriptor for 3D surface matching are demonstrated by the results obtained from five benchmark datasets.

In this paper, the CIM model is used to evaluate and manage the safety risk of subway construction. Taking Nanchang Metro Line 4 bid section 3 as an example, the total safety risk of the project construction is estimated, and the corresponding countermeasures are proposed, which is of certain significance to avoid and respond to the construction project risk. The results show that the possibility of moderate total safety risk in the construction of the third bid section of Nanchang Metro Line 4 is relatively large, and the probability is about 40%.

Accurate hand-eye and robot-world calibration remains crucial for vision-guided robotic applications. Existing methods predominantly focus on deriving closed-form or nonlinear optimization solutions for hand-eye equation AX=XB or hand-eye-robot-world calibration equation AX=YB under various rotation parameterizations, while the critical role of input data refinement on calibration accuracy has been largely overlooked. This paper addresses this gap by proposing a pose graph optimization model that strategically enhances the precision of key pose chains in robotic vision systems, particularly under significant discrepancies between robotic positioning and visual measurement accuracies. Building upon the optimized pose chains associated with the hand-eye and robot-world parameters, a pair of dual equations for X and Y is then constructed and solved via our proposed Kronecker product-based least squares algorithm, allowing simultaneous and accurate estimation on rotation matrices and translation components of X and Y. Simulation and real-world experimental results have validated the superiority of the proposed pose graph optimization model, under significant robot-vision precision disparities. Moreover, even when the error magnitudes between the robot and visual measurement system are comparable, the proposed method achieves superior accuracy for both the hand-eye and robot-world parameters compared to nine state-of-the-art approaches.