Simultaneous Localization and Mapping by Cooperative Robots

The process of simultaneously localizing a mobile robot in an unknown environment while building a map of the environment, known as Simultaneous Localization and Mapping (SLAM), has been a central research topic in robotics in recent years. SLAM in dynamic and complex environments remains an open research problem. If properly designed, a team of robots could significantly increase the speed of mapping and make the system more robust since overlapping information can be used to verify proper functionality of the individual robots and the failure of a single robot does not hinder the overall mission. The purpose of this project is to implement SLAM in a dynamic indoor environment using multiple ground robots. This task requires the combination of inertial and visual sensors as well as active range-finders such as a Kinect Infrared Sensor. The first stage of this project focused on gaining a familiarity with the robotic platforms and software that were to be used by developing methods of data acquisition, sensor fusion, and map building. Also, a camera was implemented in order to detect moving objects and remove them from the map. Future steps of the project include combining local maps from single robots into a global map and gaining a familiarity with localization given the environment map, depth information, and on-board sensor measurements ultimately leading to the implementation of cooperative SLAM

Junior Recital: Kevin Krivosik

Kemp Recital HallMarch 29, 2014Saturday Afternoon3:30 p.m

Senior Recital: Kevin Daniel Rahtjen, Oboe and English Horn

Kemp Recital Hall November 30, 2018 Friday Evening 6:00p.m

From Data Fusion to Knowledge Fusion

The task of {\em data fusion} is to identify the true values of data items (eg, the true date of birth for {\em Tom Cruise}) among multiple observed values drawn from different sources (eg, Web sites) of varying (and unknown) reliability. A recent survey\cite{LDL+12} has provided a detailed comparison of various fusion methods on Deep Web data. In this paper, we study the applicability and limitations of different fusion techniques on a more challenging problem: {\em knowledge fusion}. Knowledge fusion identifies true subject-predicate-object triples extracted by multiple information extractors from multiple information sources. These extractors perform the tasks of entity linkage and schema alignment, thus introducing an additional source of noise that is quite different from that traditionally considered in the data fusion literature, which only focuses on factual errors in the original sources. We adapt state-of-the-art data fusion techniques and apply them to a knowledge base with 1.6B unique knowledge triples extracted by 12 extractors from over 1B Web pages, which is three orders of magnitude larger than the data sets used in previous data fusion papers. We show great promise of the data fusion approaches in solving the knowledge fusion problem, and suggest interesting research directions through a detailed error analysis of the methods.Comment: VLDB'201

Agens-Präferenzen bei der Verarbeitung von satzinitialen Referenten mit Topikmarkierung im Japanischen?

Beim Sprachverstehen werden eine limitierte Anzahl an sprachspezifischen syntaktischen und semantischen linguistic cues herangezogen, um einem Referenten online die thematische Rolle des Agens vor dem Erscheinen des finiten Verbs zuzuweisen. Die Ergebnisse dieser Studie zeigen Evidenz dafür, dass japanische L1-Sprecher unter Abwesenheit eindeutiger morphosyntaktischer und semantischer Informationen den linguistic cue der ersten Argumentposition dazu nutzen, um in der ‚N1-TOP N2’ Struktur den satzinitialen Referenten als Agens des Satzes zu interpretieren. Diese Behauptung wird dadurch begründet, dass sich die Probanden bei 2AFC-Aufgaben signifikant häufiger für das Bild entschieden, auf dem das Agens des gezeigten Ereignisses identisch mit dem satzinitialen Referenten des Teilsatzes ist. Es wird angenommen, dass sich aus diesen Befunden eine allgemeine Präferenz für die Agent-first Strategie bei der inkrementellen Verarbeitung von japanischen Sätzen durch L1-Sprecher ableiten lässt

We Know it\u27s Service, But What are They Learning? Preservice Teachers\u27 Understandings of Diversity

A great deal of research on multiculturalism looks at different approaches to multicultural education and visions of multicultural teaching and learning. Though some research theorizes about how preservice teachers might learn about race or gender, there is very little work that helps teacher educators understand what learning about diversity more broadly, might look like. This study uses the conceptual framework developed by Paine to raise questions about and illuminate differences in the learning outcomes of preservice teachers who participated in two similar yet notably different service-learning experiences. Through examinations of writing tasks we find that teacher learning did indeed depend on the opportunities to learn provided by service-learning placements. Service-learning experiences that facilitated non-traditional power dynamics, engaged out-of-school contexts, and connected to teaching pedagogy were associated with more complex understandings of diversity. We suggest that attention to the relationships between service experiences and learning will help us better manage service learning limitations, better understand the impact of service-learning, and better understand the opportunities to learn inherent in such activities