Accurate registration between LiDAR (Light Detection and Ranging) point clouds and semantic 3D city models is a fundamental topic in urban digital twinning and a prerequisite for downstream tasks, such as digital construction, change detection and model refinement. However, achieving accurate LiDAR-to-Model registration at individual building level remains challenging, particularly due to the generalization uncertainty in semantic 3D city models at the Level of Detail 2 (LoD2). This paper addresses this gap by proposing L2M-Reg, a plane-based fine registration method that explicitly accounts for model uncertainty. L2M-Reg consists of three key steps: establishing reliable plane correspondence, building a pseudo-plane-constrained Gauss-Helmert model, and adaptively estimating vertical translation. Experiments on three real-world datasets demonstrate that L2M-Reg is both more accurate and computationally efficient than existing ICP-based and plane-based methods. Overall, L2M-Reg provides a novel building-level solution regarding LiDAR-to-Model registration when model uncertainty is present.

In this study, to perform the robotic stroke motions following the shape of the human back similar to the stroke motions by humans, in contrast to the conventional robotic stroke motion with a linear trajectory, we propose a trajectory generation method for a robotic stroke motion following the shape of the human back. We confirmed that the accuracy of the method's trajectory was close to that of the actual stroking motion by a human. Furthermore, we conducted a subjective experiment to evaluate the psychological effects of the proposed stroke motion in contrast to those of the conventional stroke motion with a linear trajectory. The experimental results showed that the actual stroke motion following the shape of the human back tended to evoke more pleasant and active feelings than the conventional stroke motion.

This article studies the collaborative transportation of a cable-suspended pipe by two quadrotors. A force-coordination control scheme is proposed, where a force-consensus term is introduced to average the load distribution between the quadrotors. Since thrust uncertainty and cable force are coupled together in the acceleration channel, disturbance observer can only obtain the lumped disturbance estimate. Under the quasi-static condition, a disturbance separation strategy is developed to remove the thrust uncertainty estimate for precise cable force estimation. The stability of the overall system is analyzed using Lyapunov theory. Both numerical simulations and indoor experiments using heterogeneous quadrotors validate the effectiveness of thrust uncertainty separation and force-consensus algorithm.

As a common method in Machine Learning, Ensemble Method is used to train multiple models from a data set and obtain better results through certain combination strategies. Stacking method, as representatives of Ensemble Learning methods, is often used in Machine Learning Competitions such as Kaggle. This paper proposes a variant of Stacking Model based on the idea of gcForest, namely Adaptive Generation Model (AGM). It means that the adaptive generation is performed not only in the horizontal direction to expand the width of each layer model, but also in the vertical direction to expand the depth of the model. For base models of AGM, they all come from preset basic Machine Learning Models. In addition, a feature augmentation method is added between layers to further improve the overall accuracy of the model. Finally, through comparative experiments on 7 data sets, the results show that the accuracy of AGM are better than its previous models.

Scale invariance is a fundamental property of ensembles of natural images [1]. Their non Gaussian properties [15, 16] are less well understood, but they indicate the existence of a rich statistical structure. In this work we present a detailed study of the marginal statistics of a variable related to the edges in the images. A numerical analysis shows that it exhibits extended self-similarity [3, 4, 5]. This is a scaling property stronger than self-similarity: all its moments can be expressed as a power of any given moment. More interesting, all the exponents can be predicted in terms of a multiplicative log-Poisson process. This is the very same model that was used very recently to predict the correct exponents of the structure functions of turbulent flows [6]. These results allow us to study the underlying multifractal singularities. In particular we find that the most singular structures are one-dimensional: the most singular manifold consists of sharp edges.

Scale invariance is a fundamental property of ensembles of natural images [1]. Their non Gaussian properties [15, 16] are less well understood, but they indicate the existence of a rich statistical structure. In this work we present a detailed study of the marginal statistics of a variable related to the edges in the images. A numerical analysis shows that it exhibits extended self-similarity [3, 4, 5]. This is a scaling property stronger than self-similarity: all its moments can be expressed as a power of any given moment. More interesting, all the exponents can be predicted in terms of a multiplicative log-Poisson process. This is the very same model that was used very recently to predict the correct exponents of the structure functions of turbulent flows [6]. These results allow us to study the underlying multifractal singularities. In particular we find that the most singular structures are one-dimensional: the most singular manifold consists of sharp edges.

Scale invariance is a fundamental property of ensembles of natural images[1]. Their non Gaussian properties [15, 16] are less well understood, but they indicate the existence of a rich statistical structure.In this work we present a detailed study of the marginal statistics of a variable related to the edges in the images. A numerical analysis shows that it exhibits extended self-similarity [3, 4, 5]. This is a scaling property stronger than self-similarity: all its moments can be expressed as a power of any given moment. More interesting, all the exponents can be predicted in terms of a multiplicative log-Poisson process. This is the very same model that was used very recently to predict the correct exponents of the structure functions of turbulent flows [6]. These results allow us to study the underlying multifractal singularities. In particular we find that the most singular structures are one-dimensional: the most singular manifold consists of sharp edges.