A key component of a mobile robot system is the ability to localize itself accurately and, simultaneously, to build a map of the environment. Most of the existing algorithms are based on laser range finders, sonar sensors or artificial landmarks. In this paper, we describe a vision-based mobile robot localization and mapping algorithm, which uses scale-invariant image features as natural landmarks in unmodified environments. The invariance of these features to image translation, scaling and rotation makes them suitable landmarks for mobile robot localization and map building. With our Triclops stereo vision system, these landmarks are localized and robot ego-motion is estimated by least-squares minimization of the matched landmarks. Feature viewpoint variation and occlusion are taken into account by maintaining a view direction for each landmark. Experiments show that these visual landmarks are robustly matched, robot pose is estimated and a consistent three-dimensional map is built. As image features are not noise-free, we carry out error analysis for the landmark positions and the robot pose. We use Kalman filters to track these landmarks in a dynamic environment, resulting in a database map with landmark positional uncertainty.

We report on a successful preliminary experience in the design and implementation of a special-purpose Abstract Interpretation based static program analyzer for the verification of safety critical embedded real-time software. The analyzer is both precise (zero false alarm in the considered experiment) and efficient (less than one minute of analysis for 10,000 lines of code). Even if it is based on a simple interval analysis, many features have been added to obtain the desired precision: expansion of small arrays, widening with several thresholds, loop unrolling, trace partitioning, relations between loop counters and other variables. The efficiency of the tool mainly comes from a clever representation of abstract environments based on balanced binary search trees.

All published fault-tolerant clock synchronization protocols are shown to result from refining a single paradigm. This allows the differera clock synchronization protocols to be compared and permits presemation of a single correctness analysis that holds for all. The paradigm is based on a reliable time source that periodically causes events; detection of such an event causes a processor to reset its clock. In a distributed system, the reliable time source can be approximated by combining the values of processor clocks using a generalization of a "fault-tolerant average", called a convergence function. The performance of a clock synchronization protocol based on our paradigm can be quantified in terms of the two parameters that characterize the behavior of the convergence function used: accuracy and precision.

A software energy estimation model is presented for a family of high performance, integrated, 32-bit embedded RISC processors. This model is significantly less complex than previous models, and yet is demonstrated to accurately predict energy consumption to within 8 % with 99 % confidence based on physical measurements. Factors such as operating frequency, source/destination registers, and operand values are explored. In view of this model, previously proposed optimizations are evaluated for potential energy savings. We conclude that, for the class of processors under discussion, a good optimizing compiler that minimizes execution time will simultaneously minimize energy consumption. 1.

Abstract—We have previously developed a mobile robot system which uses scale-invariant visual landmarks to localize and simultaneously build three-dimensional (3-D) maps of unmodified environments. In this paper, we examine global localization, where the robot localizes itself globally, without any prior location estimate. This is achieved by matching distinctive visual landmarks in the current frame to a database map. A Hough transform approach and a RANSAC approach for global localization are compared, showing that RANSAC is much more efficient for matching specific features, but much worse for matching nonspecific features. Moreover, robust global localization can be achieved by matching a small submap of the local region built from multiple frames. This submap alignment algorithm for global localization can be applied to map building, which can be regarded as alignment of multiple 3-D submaps. A global minimization procedure is carried out using the loop closure constraint to avoid the effects of slippage and drift accumulation. Landmark uncertainty is taken into account in the submap alignment and the global minimization process. Experiments show that global localization can be achieved accurately using the scale-invariant landmarks. Our approach of pairwise submap alignment with backward correction in a consistent manner produces a better global 3-D map. Index Terms—Global localization, map building, mobile robots, visual landmarks. I.

. The Bayesian approach to probability theory is presented as an alternative to the currently used long-run relative frequency approach, which does not offer clear, compelling criteria for the design of statistical methods. Bayesian probability theory offers unique and demonstrably optimal solutions to well-posed statistical problems, and is historically the original approach to statistics. The reasons for earlier rejection of Bayesian methods are discussed, and it is noted that the work of Cox, Jaynes, and others answers earlier objections, giving Bayesian inference a firm logical and mathematical foundation as the correct mathematical language for quantifying uncertainty. The Bayesian approaches to parameter estimation and model comparison are outlined and illustrated by application to a simple problem based on the gaussian distribution. As further illustrations of the Bayesian paradigm, Bayesian solutions to two interesting astrophysical problems are outlined: the measurement of wea...

this paper are applicable to a wide variety of experimental paradigms. However, it may help to conceptualise the stimulus as for example which object, of some set, is being viewed by the experimental subject; indeed, data from such an experiment are examined later in this paper. Consider the stimuli to be taken from a discrete set S with S elements, each occurring with probability P (s). The probability of events with response r is denoted as P (r), and the joint probability distribution as P (s; r).

Our mobile robot system uses scale-invariant visual landmarks to localize itself and build a 3D map of the environment simultaneously. As image features are not noise-free, we carry out error analysis and use Kalman Filters to track the 3D landmarks, resulting in a database map with landmark positional uncertainty. By matching a set of landmarks as a whole, our robot can localize itself globally based on the database containing landmarks of sucient distinctiveness. Experiments show that recognition of position within a map without any prior estimate can be achieved using the scale-invariant landmarks.

Our research empirically analyzes consumer behavior at Internet shopbots — sites that allow consumers to make “one-click ” price comparisons for product offerings from multiple retailers. By allowing researchers to observe exactly what information the consumer is shown and their search behavior in response to this information, shopbot data has unique strengths for analyzing consumer behavior. Furthermore, the method in which the data is displayed to consumers lends itself to a utility-based evaluation process, consistent with econometric analysis techniques. While price is an important determinant of customer choice, we find that, even among shopbot consumers, branded retailers and retailers a consumer visited previously hold significant price advantages in head-to-head price comparisons. Further, customers are very sensitive to how the total price is allocated among the item price, the shipping cost, and tax, and are also quite sensitive to the ordinal ranking of retailer offerings with respect to price. We also find that consumers use brand as a proxy for a retailer’s credibility with regard to non-contractible aspects of the product bundle such as shipping time. In each case our models accurately predict consumer behavior out of sample, suggesting

Monte Carlo simulation is one of the main applications involving the use of random number generators. It is also one of the best methods of testing the randomness properties of such generators, by comparing results of simulations using di erent generators with each other, or with analytic results. Here we compare the performance ofsome popular random number generators by high precision Monte Carlo simulation of the 2-d Ising model, for which exact results are known, using the Metropolis, Swendsen-Wang, and Wol Monte Carlo algorithms. Many widely used generators that perform well in standard statistical tests are shown to fail these Monte Carlo tests.