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Six-DoF (Six-degree-of-freedom) pose localization based on 3D point clouds is a challenging task for LBSs (localization-based services). This paper proposes a robust and efficient method that uses multimodal information (vision and Wi-Fi signal information) to estimate the 6-DoF pose of an RGBD camera on a robot with respect to complex 3D textured models of the indoor environment that can contain more than 650,000,000 points. Our developed method narrows the search scope, which delimits boundaries initially using the Wi-Fi location system and applies an environment-adaptive approach to determine the radius of the search sphere based on the signal stability of the Wi-Fi location system. In addition, we propose an algorithm for estimating a novel correspondence between local points with a 3D submap by combining 3D points and surface normals to acquire absolute poses from noisy and outlier-contaminated matching point sets for RGBD sensors in dynamic indoor scenes. Then, a novel two-level spatial verification strategy is used to estimate an accurate pose, which includes the use of a RANSAC (Random Sample Consensus) algorithm for identification and a direct least-square method to acquire the pose from the inliers. The proposed method has been implemented and tested extensively in various indoor scenes. The experimental results demonstrate that the Wi-Fi-aided localization system can efficiently localize a mobile robot in a variety of large-scale 3D point cloud datasets to realize stable time consumption and similar performance to state-of-the-art methods.

In this article, the problem of sampled-data control is considered for fuzzy Markovian jump systems (FMJSs) with actuator saturation. Firstly, the mode-dependent lyapunov functional is constructed, which consists of a two-sided closed-loop function. On the basis of this function, stochastically stable criteria are presented in the form of linear matrix inequalities (LMIs). Then, based on the stochastically stable criteria, mode-dependent sampled-data controllers are designed for FMJSs. As a corollary, stable criteria and controller design are also proposed for the general aperiodic sampled-data linear system with actuator saturation. The improved LMI-based optimization conditions are obtained to estimate the region of attraction. Finally, numerical examples are provided to show the significant improvement of the presented method.

Quantifying regional myocardial motion and deformation from cardiac magnetic resonance imaging (MRI) plays an important role in clinical applications. In this paper, we present a novel approach for the estimation of left ventricle myocardial motion based on iterative deformable graph matching for cine MRI. Graph matching is a shape matching approach that can be used to determine the correspondence between two objects. However, existing graph matching algorithms are unsuitable for applications with large deformations. In this paper, we propose an iterative deformable graph matching framework for estimating the correspondence between points extracted from left ventricle myocardium at the end-diastolic and end-systolic phases to estimate cardiac motion. A new cost function for graph matching is defined to measure the discrepancy between the nodes and edges of two graphs under a transformation. By introducing a spatial transformation with a sparsity constraint, we can estimate a robust deformation field, alleviating the influence of inevitable graph mismatches. The correspondence between points is then updated by mapping the source graph using the estimated transformation. The cost function is optimized by alternatively optimizing for correspondence and spatial transformation. Furthermore, we propose a multilayer framework to improve correspondence accuracy using a bottom-up matching procedure. This framework estimates the deformation field between an image at the end-systolic phase and an image at the end-diastolic phase in an MRI sequence. Evaluations of two public cardiac datasets indicate that the proposed framework outperforms traditional graph matching algorithms in accuracy and robustness.

Estimating robust transformations based on noisy landmark correspondences is challenging and cannot be ensured to be exact. In this paper, we propose a novel sparse transformation model based on corresponding landmarks. First, we construct a new transformation model that uses compact supported radial basis functions (CSRBFs) with multiple supports, with a least-squares cost function constrained by the l1 and l2 norms of the elastic and affine deformation coefficients used to estimate the CSRBF coefficients. This sparse model can be used to select CSRBFs with different supports and construct a robust deformation field. Then, the relationship between the CSRBF coefficients and the bending energy of the deformation field is analyzed in a reproducing kernel Hilbert space; this bending energy is introduced into the cost function as a regularization term. The cost function is optimized by using the fast iterative shrinkagethreshold algorithm to compute coefficients in the transformation model. To demonstrate the performance of our sparse transformation model, we combine it with robust point matching to simultaneously estimate the correspondence and transformation between landmarks. Experiments on synthetic data, brain images, and cardiac images show that the transformations estimated by our sparse transformation model are robust to noised landmark correspondences, preserving registration accuracy, minimizing the bending energy of the deformation field, and preserving the topology of the deformation field.