Eine pixelgenaue semantische Segmentierung bildet die Grundlage fur ein umfassendes Szenenverstan... more Eine pixelgenaue semantische Segmentierung bildet die Grundlage fur ein umfassendes Szenenverstandnis. Semantisches Wissen uber die Struktur und den Aufbau von Indoor-Szenen kann mobilen Robotern bei verschiedenen Aufgaben nutzlich sein. Unter Anderem kann dadurch die Lokalisierung, die Hindernisvermeidung, die gezielte Navigation zu semantischen Entitaten oder die Mensch-Maschine-Interaktion unterstutzt werden. Durch den Einsatz von effizienten RGB-Verfahren konnten zuletzt bereits gute Segmentierungsergebnisse erzielt werden. Bei zusatzlicher Berucksichtigung von Tiefendaten kann die Segmentierungsleistung in der Regel noch weiter verbessert werden. In dieser Masterarbeit werden daher Verfahren zur effizienten semantischen Segmentierung und zur RGBD-Segmentierung kombiniert. Auf Basis einer breiten Recherche zu beiden Themengebieten wird ein eigener, effizienter Deep-Learning-basierter RGBD-Segmentierungsansatz entwickelt. Mittels ausfuhrlicher Experimente zu verschiedenen Bestand...
2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2020
In order to deploy service robots in environments where they encounter and/or cooperate with pers... more In order to deploy service robots in environments where they encounter and/or cooperate with persons, one important key factor is human acceptance. Hence, information on which upcoming actions of the robot are based has to be made transparent and understandable to the human. However, considering the restricted power resources of mobile robot platforms, systems for visualization not only have to be expressive but also energy efficient. In this paper, we applied the well-known technique of laser scanning on a mobile robot to create a novel system for intention visualization and human-robot-interaction. We conducted user tests to compare our system to a low-power consuming LED video projector solution in order to evaluate the suitability for mobile platforms and to get human impressions of both systems. We can show that the presented system is preferred by most users in a dynamic test setup on a mobile platform.
Deep Learning approaches have recently raised the bar in many fields, from Natural Language Proce... more Deep Learning approaches have recently raised the bar in many fields, from Natural Language Processing to Computer Vision, by leveraging large amounts of data. However, they could fail when the retrieved information is not enough to fit the vast number of parameters, frequently resulting in overfitting and, therefore, in poor generalizability. Few-Shot Learning aims at designing models which can effectively operate in a scarce data regime, yielding learning strategies that only need few supervised examples to be trained. These procedures are of both practical and theoretical importance, as they are crucial for many real-life scenarios in which data is either costly or even impossible to retrieve. Moreover, they bridge the distance between current data-hungry models and human-like generalization capability. Computer Vision offers various tasks which can be few-shot inherent, such as person re-identification. This survey, which to the best of our knowledge is the first tackling this p...
Eine pixelgenaue semantische Segmentierung bildet die Grundlage fur ein umfassendes Szenenverstan... more Eine pixelgenaue semantische Segmentierung bildet die Grundlage fur ein umfassendes Szenenverstandnis. Semantisches Wissen uber die Struktur und den Aufbau von Indoor-Szenen kann mobilen Robotern bei verschiedenen Aufgaben nutzlich sein. Unter Anderem kann dadurch die Lokalisierung, die Hindernisvermeidung, die gezielte Navigation zu semantischen Entitaten oder die Mensch-Maschine-Interaktion unterstutzt werden. Durch den Einsatz von effizienten RGB-Verfahren konnten zuletzt bereits gute Segmentierungsergebnisse erzielt werden. Bei zusatzlicher Berucksichtigung von Tiefendaten kann die Segmentierungsleistung in der Regel noch weiter verbessert werden. In dieser Masterarbeit werden daher Verfahren zur effizienten semantischen Segmentierung und zur RGBD-Segmentierung kombiniert. Auf Basis einer breiten Recherche zu beiden Themengebieten wird ein eigener, effizienter Deep-Learning-basierter RGBD-Segmentierungsansatz entwickelt. Mittels ausfuhrlicher Experimente zu verschiedenen Bestand...
2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2020
In order to deploy service robots in environments where they encounter and/or cooperate with pers... more In order to deploy service robots in environments where they encounter and/or cooperate with persons, one important key factor is human acceptance. Hence, information on which upcoming actions of the robot are based has to be made transparent and understandable to the human. However, considering the restricted power resources of mobile robot platforms, systems for visualization not only have to be expressive but also energy efficient. In this paper, we applied the well-known technique of laser scanning on a mobile robot to create a novel system for intention visualization and human-robot-interaction. We conducted user tests to compare our system to a low-power consuming LED video projector solution in order to evaluate the suitability for mobile platforms and to get human impressions of both systems. We can show that the presented system is preferred by most users in a dynamic test setup on a mobile platform.
Deep Learning approaches have recently raised the bar in many fields, from Natural Language Proce... more Deep Learning approaches have recently raised the bar in many fields, from Natural Language Processing to Computer Vision, by leveraging large amounts of data. However, they could fail when the retrieved information is not enough to fit the vast number of parameters, frequently resulting in overfitting and, therefore, in poor generalizability. Few-Shot Learning aims at designing models which can effectively operate in a scarce data regime, yielding learning strategies that only need few supervised examples to be trained. These procedures are of both practical and theoretical importance, as they are crucial for many real-life scenarios in which data is either costly or even impossible to retrieve. Moreover, they bridge the distance between current data-hungry models and human-like generalization capability. Computer Vision offers various tasks which can be few-shot inherent, such as person re-identification. This survey, which to the best of our knowledge is the first tackling this p...
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Papers by Mona Köhler