Percorrer por autor "Bento, Luis Conde"
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- Point Cloud Alignment for Deposited Material Assessment in Tunnel EnvironmentsPublication . Teixeira, Abel; Costelha, Hugo; Neves, Carlos; Bento, Luis CondeThe assessment of deposited material in tunnel reinforcement operations can be performed using a 3D model generated from multiple scans. For this purpose, an accurate alignment of the scanned models is required. Aligning existing structure model with data scanned after surface deformations can be challenging, particularly if reference markers are not available or were displaced. For scenarios where the surrounding structure is largely changed, certain procedures can be adapted when processing the scanned data to achieve consistent alignment between scanned and reference structure models. This work proposes a methodology to cope with these situations, analysing the impact of different approaches. Experiments were performed in a realistic scenario related with shotcrete of railway tunnels wall surfaces, with the results showing the applicability of the developed work. The proposed procedure relies in highlighting the importance of specific points that describe the same feature in the reference and aligning PC. The proposed methodology achieved an RMS difference of 0.0173 m, which lead to a drastic improvement in the point cloud alignment compared to the use of standard ICP algorithm without data preprocessing, which achieved 0.0518 m in the studied use-case.
- Set-Membership Position Estimation With GNSS Pseudorange Error Mitigation Using Lane-Boundary MeasurementsPublication . Bento, Luis Conde; Bonnifait, Philippe; Nunes, Urbano J.Model-based positioning methods involve nonlinear equations as is the case when using satellite pseudoranges on global navigation satellite systems (GNSSs) and local measurements on road features. As these are nonlinear models, classical estimation methods cannot provide guaranteed position estimation and can converge to local optima, sometimes far away from the global optimum or the true value. Based on interval analysis, set inversion, and constraints propagation on real values provide a framework that guarantees to find the true position with a characterized confidence domain. This paper describes an error bounded set membership algorithm that computes the absolute position of a road vehicle by using raw GNNS pseudoranges, lane boundary measurements, and a 2D road network map as geometric constraints. The algorithm is based on set inversion using interval analysis, and bounds are set on the measurements by taking into account a chosen risk. The GNSS pseudoranges errors are modeled carefully, and road constraints are formalized to provide additional information in the data fusion process. The proposed algorithm, named lane boundary augmented set-membership GNSS positioning (LB-ASGP), provides a novel and inexpensive approach to improve position estimation performance for road vehicles guaranteeing the enclosure of the computed solution with high confidence. Results from simulations and field experiments show that the LB-ASGP significantly reduces GNSS errors in the direction perpendicular to the lane thanks to the lane boundary measurements.
- Survey of SLAM Algorithms with ROS SupportPublication . Teixeira, Abel; Costelha, Hugo; Bento, Luis Conde; Neves, CarlosSimultaneous Localization and Mapping (SLAM) algorithms are a key component in enabling autonomous navigation for robotic systems. This study presents a comprehensive assessment of state-of-the-art SLAM algorithms, focusing exclusively on those with Robot Operating System (ROS) support. The study aims to provide insights into the computational performance of these algorithms by leveraging the benchmark results reported in their respective studies. Each algorithm's performance metrics, as reported in their benchmark studies, are analyzed and compared. This compara-ive analysis not only highlights the strengths and weaknesses of individual algorithms but also provides a broader understanding of their applicability across diverse robotic platforms and environments. Overall, this study contributes to the advancement of SLAM research by offering a comparative evaluation tailored to ROS-supported algorithms. The findings serve as a valuable resource to make informed decisions regarding the selection and implementation of SLAM solutions in real-world applications.