Robust Coarse-to-Fine Registration Scheme for Mobile Laser Scanner Point Clouds Using Multiscale Eigenvalue Statistic-Based Descriptor

To overcome the drawbacks of pairwise registration for mobile laser scanner (MLS) point clouds, such as difficulty in searching the corresponding points and inaccuracy registration matrix, a robust coarse-to-fine registration method is proposed to align different frames of MLS point clouds into a common coordinate system. The method identifies the correct corresponding point pairs from the source and target point clouds, and then calculates the transform matrix. First, the performance of a multiscale eigenvalue statistic-based descriptor with different combinations of parameters is evaluated to identify the optimal combination. Second, based on the geometric distribution of points in the neighborhood of the keypoint, a weighted covariance matrix is constructed, by which the multiscale eigenvalues are calculated as the feature description language. Third, the corresponding points between the source and target point clouds are estimated in the feature space, and the incorrect ones are eliminated via a geometric consistency constraint. Finally, the estimated corresponding point pairs are used for coarse registration. The value of coarse registration is regarded as the initial value for the iterative closest point algorithm. Subsequently, the final fine registration result is obtained. The results of the registration experiments with Autonomous Systems Lab (ASL) Datasets show that the proposed method can accurately align MLS point clouds in different frames and outperform the comparative methods.

Linear eigenvalue statistics of random matrices with a variance profile

We give an upper bound on the total variation distance between the linear eigenvalue statistic, properly scaled and centered, of a random matrix with a variance profile and the standard Gaussian random variable. The second-order Poincaré inequality-type result introduced in [S. Chatterjee, Fluctuations of eigenvalues and second order poincaré inequalities, Prob. Theory Rel. Fields 143(1) (2009) 1–40.] is used to establish the bound. Using this bound, we prove central limit theorem for linear eigenvalue statistics of random matrices with different kind of variance profiles. We re-establish some existing results on fluctuations of linear eigenvalue statistics of some well-known random matrix ensembles by choosing appropriate variance profiles.

ANALISIS PENGARUH SUKU BUNGA, PENDAPATAN NASIONAL DAN INFLASI TERHADAP NILAI TUKAR NOMINAL : PENDEKATAN DENGAN COINTEGRATION DAN ERROR CORRECTION MODEL (ECM)

Abstrak. Penelitian ini bertujuan untuk menganalisa pengaruh Suku bunga, inflasi, dan Pendapatan Nasional terhadap nilai tukar rupiah terhadap dollar baik hubungan keseimbangan jangka panjang maupun keseimbangan jangka pendek data empiris tahun 1980-2015 (36 tahun) dengan menggunakan data sekunder. Metode penelitian yang digunakan adalah regresi linier berganda metoda OLS. Metoda penelitian ini menggunakan mendekatan dengan cointegration dan error correction model (ECM) dengan sebelumnya melallui beberapa tahapan pengujian statistic lainnya. Hasil dalam penelitian dengan cointegration (Johansen Cointegration test) menunjukkan bahwa semua variable bebas (inflasi, pendapatan nasional dan suku bunga) dan variable tak bebas (nilai tukar) telah terjadi hubungan keseimbangan (equilibrium) dalam jangka panjang, hal ini dibuktikan dengan hasil uji tersebut dimana nilai trace statistic sebesar 102.1727 jauh lebih besar dari nilai kritis (5%) sebesar 47.85613. Selain itu pula hasil dari Maximum Eigenvalue Statistic yaitu dengan hasil sebesar 36,7908 lebih besar dari nilai kritis 5%. Sebesar 27,584434. Sementara hasil dari uji koreksi kesalahan model (ECM) bahwa hanya variable inflasi, suku bunga dan residual yang signifikan, sementara variable pendapatan nasional tidak signifikan. Hal ini yang berarti bahwa variable inflasi dan suku bungan mempunyai hubungan jangka pendek terhadap nilai tukar, hal ini terlihat dari nilai Probabilitas (Prob.) masing- masing variable dibawan 0,05 (5%), selain itu koefisien residual pada hasil uji ECM adalah -0,732447, hal ini menunjukan bahwa koreksi kesalah (error correction term) adalah sebesar 73,24% dan significant.