In this paper we introduce an adaptive cost-function for pointcloud registration. The algorithm automatically estimates the sensor noise, which is important for generalization across different sensors and environments. Through experiments on real and synthetic data, we show significant improvements in accuracy and robustness over state-of-the-art solutions.

In this work, we study the k-means cost function. The (Euclidean) k-means problem can be described as follows: given a dataset X ⊆ R and a positive integer k, find a set of k centers C ⊆ R such that Φ(C,X) def = ∑ x∈X minc∈C ||x− c|| 2 is minimized. Let ∆k(X) def = minC⊆Rd Φ(C,X) denote the cost of the optimal k-means solution. It is simple to observe that for any dataset X, ∆k(X) decreases as ...

The industrialization of the world, increase in population and mismanagement of the available parking space has resulted in parking problems. There is a need for an intelligent and reliable system which can be used for searching the unoccupied parking facility, to reduce the cost of leasing people and for better use of resources for car-park owners. This paper introduces an algorithm to increas...

Optimization is one of the most important issues in all fields of science and engineering. There are two main categories for optimization problems: continues optimization and discrete optimization. Traditional methods, such as gradient descent, are used for solving continues optimization problems, But for discrete optimization, traditional and many new algorithms are introduced. Due to long tim...

The goal of this paper is to present a novel stochastic cost function for binocular stereo vision that delivers statistics about the most probable disparities on the pixel level. We drive these statistics by many independent stochastic processes so that robustness to outliers can be achieved. Each of these stochastic processes may be understood as an individual who is requested to deliver his o...

Parametric cost analysis uses equations to map measurable system attributes into cost. The measures of the system attributes are called metrics. The equations are called cost estimating relationships (CER's), and are obtained by the analysis of cost and technical metric data of products analogous to those to be estimated. Examples of system metrics include mass, power, failure_rate, mean_time_t...

In the discussion in Chapter 2, we assumed that we had only data on marketlevel prices and quantities, together with cost and demand shifters. In this case, we were forced to make inferences about costs from the market outcomes together with an equilibrium assumption on the model. We saw how the equilibrium assumption can influence the resulting estimates of cost. Inside of learning about costs...

John A. Hertz Nordita Blegdamsvej 17 2100 Copenhagen Denmark We introduce a cost function for learning in feed-forward neural networks which is an explicit function of the internal representation in addition to the weights. The learning problem can then be formulated as two simple perceptrons and a search for internal representations. Back-propagation is recovered as a limit. The frequency of s...

in this paper we intend to generate some set of optimal trajectories according to the number of control points has been applied for parameterizing those using b-spline curves. the trajectories are used to generate an optimal locomotion gait in a crawling worm-like robot. due to gait design considerations it is desired to minimize the required torques in a cycle of gait. similar to caterpillars,...