Trajectory Following using Nonlinear Model Predictive Control and 3D Point-Cloud-based Localization for Autonomous Driving

Babu, Ajish; Yurtdas, Kerim Yener; Koch, Christian Ernst Siegfried; Yüksel, Mehmed

doi:10.1109/ecmr.2019.8870956

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…In practice, one of the key tasks to solve is how to align pre-built 3D models against the scanned points of real buildings. The aligning results help generate complete and accurate digital models, contributing largely to measuring and applications such as simultaneous localization and mapping (SLAM), 3D reconstruction [21][22][23][24], localization [25][26][27][28], and pose estimation [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Feature Consistent Point Cloud Registration in Building Information Modeling

Jiang

Lasang²,

Nader³

et al. 2022

Sensors

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Point Cloud Registration contributes a lot to measuring, monitoring, and simulating in building information modeling (BIM). In BIM applications, the robustness and generalization of point cloud features are particularly important due to the huge differences in sampling environments. We notice two possible factors that may lead to poor generalization, the normal ambiguity of boundaries on hard edges leading to less accuracy in transformation; and the fact that existing methods focus on spatial transformation accuracy, leaving the advantages of feature matching unaddressed. In this work, we propose a boundary-encouraging local frame reference, the PyramidFeature(PMD), consisting of point-level, line-level, and mesh-level information to extract a more generalizing and continuous point cloud feature to encourage the knowledge of boundaries to overcome the normal ambiguity. Furthermore, instead of registration guided by spatial transformation accuracy alone, we suggest another supervision to extract consistent hybrid features. A large number of experiments have demonstrated the superiority of our PyramidNet (PMDNet), especially when the training (ModelNet40) and testing (BIM) sets are very different, PMDNet still achieves very high scalability.

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