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

Roboterwettbewerb im Ingenieurstudium: interdisziplinäres Projektpraktikum Autonome Mobile Roboter

Das hier beschriebene "Projektpraktikum" stellt selbständiges Lernen und Arbeiten in interdisziplinären Teams in den Vordergrund. Studierende der Elektrotechnik, Informationstechnik, Informatik und Mechatronik haben neun Monate Zeit, um einen mobilen Roboter zu programmieren und treten dann in einem Wettbewerb gegeneinander an

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