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This paper mainly studies the adjustment model with interval constraints，in which the adjustment problem is transformed into a quadratic programming problem with interval constraints. A new adjustment al- gorithm with interval constraints is presented, and the accuracy of the parameter solution is evaluated, in which the active set algorithm and unconstrained conjugate gradient optimization algorithm are used to parti- tion and reconstruct the quadratic problems. Because the projection gradient method can rapidly change the composition of the active constraint set, the new algorithm is more efficient than the classical active set meth- od, which can reduce the uncertainty of the model, and maintain the statistical, geometric or physical meaning of priori information, and is suitable for solving large-scale adjustment problems with interval constraints. [ABSTRACT FROM AUTHOR]

Exiting parallel DEM preprocessing algorithms that do not consider parallel granularity. This paper presents a parallel DEM preprocessing algorithm with granularity control based on the a-nalysis of the sequential algorithm proposed by Moran and Vezina (M&-V algorithm). A Message Passing Interface (MPD library is applied to implement the parallel algorithm. The parallel performance of the proposed algorithm is assessed by two gridded DEMs with different sizes on a multi-node Linux cluster. The application results show that the parallel M&-V algorithm can complete the computing tasks when filling sinks and removing flat areas at any granuality, and it outputs an optimal granularity to achieve the best parallel performance for a given DEM dataset. [ABSTRACT FROM AUTHOR]

As one of the core steps in point cloud data processing, the registration result has great influences on the subsequent data operations. Traditional precise registration methods mainly depend on artificial targets and feature points. These methods are limited by the external environment, initial conditions, feature points are not easy to find and so on. To overcome the limitation, this paper proposes an improved Particle Swarm optimization (PSO) algorithm. Using the sum of normal vectors' cross products to define the fitness function, the current algorithm applies an efficient "Universal Search" and implements scattered cloud data registration based on the best registration points. By the experiment with the cloud data received by a multi-station scanning of a high steep slope rock and comparing the result with the classical algorithms such as ICP, the improved PSO algorithm is proved to be feasible, efficient and stable. It can effectively solve the problem of the targets or the feature points are not easy to find in registration process. [ABSTRACT FROM AUTHOR]