Incrementally Learned Mixture Models for GNSS Localization

GNSS localization is an important part of today's autonomous systems, although it suffers from non-Gaussian errors caused by non-line-of-sight effects. Recent methods are able to mitigate these effects by including the corresponding distributions in the sensor fusion algorithm. However, these approaches require prior knowledge about the sensor's distribution, which is often not available. We introduce a novel sensor fusion algorithm based on variational Bayesian inference, that is able to approximate the true distribution with a Gaussian mixture model and to learn its parametrization online. The proposed Incremental Variational Mixture algorithm automatically adapts the number of mixture components to the complexity of the measurement's error distribution. We compare the proposed algorithm against current state-of-the-art approaches using a collection of open access real world datasets and demonstrate its superior localization accuracy.Comment: 8 pages, 5 figures, published in proceedings of IEEE Intelligent Vehicles Symposium (IV) 201

Automatically generated acceptance test: A software reliability experiment

This study presents results of a software reliability experiment investigating the feasibility of a new error detection method. The method can be used as an acceptance test and is solely based on empirical data about the behavior of internal states of a program. The experimental design uses the existing environment of a multi-version experiment previously conducted at the NASA Langley Research Center, in which the launch interceptor problem is used as a model. This allows the controlled experimental investigation of versions with well-known single and multiple faults, and the availability of an oracle permits the determination of the error detection performance of the test. Fault interaction phenomena are observed that have an amplifying effect on the number of error occurrences. Preliminary results indicate that all faults examined so far are detected by the acceptance test. This shows promise for further investigations, and for the employment of this test method on other applications

Fault-tolerance of a neural network solving the traveling salesman problem

This study presents the results of a fault-injection experiment that stimulates a neural network solving the Traveling Salesman Problem (TSP). The network is based on a modified version of Hopfield's and Tank's original method. We define a performance characteristic for the TSP that allows an overall assessment of the solution quality for different city-distributions and problem sizes. Five different 10-, 20-, and 30- city cases are sued for the injection of up to 13 simultaneous stuck-at-0 and stuck-at-1 faults. The results of more than 4000 simulation-runs show the extreme fault-tolerance of the network, especially with respect to stuck-at-0 faults. One possible explanation for the overall surprising result is the redundancy of the problem representation

Comparing several implementations of two recently published feature detectors

Abstract: Detecting, identifying, and recognizing salient regions or feature points in images is a very important and fundamental problem to the computer vision and robotics community. Tasks like landmark detection and visual odometry, but also object recognition benefit from stable and repeatable salient features that are invariant to a variety of effects like rotation, scale changes, view point changes, noise, or change in illumination conditions. Recently, two promising new approaches, SIFT and SURF, have been published. In this paper we compare and evaluate how well different available implementations of SIFT and SURF perform in terms of invariancy and runtime efficiency. 1

Agency, Structures and Peru: Action and in-action during 1980-2000

The Shining Path along with Alberto Fujimori\u27s presidency in Peru (encompassing the years 1980-2000) created a terrifying chaos that was wound up in both state and insurgent terrorism, corruption, and massacres. While this chaos is inextricably linked to Peru\u27s history, I fear is being all too quickly forgotten. My main motivation behind this research has been to take a step towards increasing awareness of these events and the many reasons behind them: the agenic nature of some versus the lack of it in others, structures developed by colonialism, and most terrifyingly of all - willful ignorance. By understanding these reasons, the hope is that society can learn to act instead of remaining ignorant to the signs of impending conflict