Advancements in robotics and mapping technology have spotlighted the development of Simultaneous Localization and Mapping (SLAM) systems as a key research area. However, the high cost of advanced SLAM systems poses a significant barrier to research and development in the field, while many low-cost SLAM systems, operating under resource constraints, fail to achieve high-precision real-time mapping and localization, rendering them unsuitable for practical applications. This paper introduces a cost-effective SLAM system design that maintains high performance while significantly reducing costs. Our approach utilizes economical components and efficient algorithms, addressing the high-cost barrier in the field. First, we developed a robust robotic platform based on a traditional four-wheeled vehicle structure, enhancing flexibility and load capacity. Then, we adapted the SLAM algorithm using the LiDAR-inertial Odometry framework coupled with the Fast Iterative Closest Point (ICP) algorithm to balance accuracy and real-time performance. Finally, we integrated the 3D multi-goal Rapidly exploring Random Tree (RRT) algorithm with Nonlinear Model Predictive Control (NMPC) for autonomous exploration in complex environments. Comprehensive experimental results confirm the system’s capability for real-time, autonomous navigation and mapping in intricate indoor settings, rivaling more expensive SLAM systems in accuracy and efficiency at a lower cost. Our research results are published as open access, facilitating greater accessibility and collaboration.

Crack is a common form of road distress and a key study of an intelligent transportation system. However, automatic pavement crack detection is a very challenging task due to noisy texture background, intensity inhomogeneity, and topology complexity. In this paper, a new pavement crack detection algorithm to address these issues is proposed. First, non-local block matching strategy and local statistical mean are put together to generate the probability map of cracks, which has advantages on automatic threshold choosing and strong resistance to intensity inhomogeneity. Second, an iterative seed points sampling algorithm is proposed, which makes full use of the area and shape of connected regions where the seeds lie in, thus exploiting high reliable crack seeds for following curves extraction. Finally, a minimum spanning tree (MST) is adopted to connect points into crack curves and employ a crack growth method to find out the cracks, which is specified to deal with complex topology of cracks. For parameters, a robust and optimal parameters selection rule is obtained by data driven method. The algorithm is compared with other state-of-the-art algorithms on two datasets. The experiment result shows that the proposed method has a better detection performance on $F1$-measure score over other methods.

Multi-user detection is an effective method to reduce multiple access interference in code division multiple access (CDMA) systems. This paper discusses a signal subspace based blind adaptive multiuser detector and a Kalman filtering blind adaptive multiuser detector. Combining them together, a new Kalman filtering blind adaptive multiuser detector based on a tracking algorithm of the signal subspace is proposed. Analysis and simulation show that the proposed blind multiuser detector achieves better suppression of multiple access interference and has a higher convergence rate.