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For many tasks in populated environments, robots need to keep track of current and future motion states of people. Most approaches to people tracking make weak assumptions on human motion such as constant velocity or acceleration. But even over a short period, human behavior is more complex and influenced by factors such as the intended goal, other people,(More)
People tracking is a key component for robots that are deployed in populated environments. Previous works have used cameras and 2D and 3D range finders for this task. In this paper, we present a 3D people detection and tracking approach using RGB-D data. We combine a novel multi-cue person detector for RGB-D data with an on-line detector that learns(More)
We present an approach to laser-based people tracking using a multi-hypothesis tracker that detects and tracks legs separately with Kalman filters, constant velocity motion models, and a multi-hypothesis data association strategy. People are defined as high-level tracks consisting of two legs that are found with little model knowledge. We extend the data(More)
With a growing number of robots deployed in populated environments, the ability to detect and track humans, recognize their activities, attributes and social relations are key components for future service robots. In this article we will consider fundamentals towards these goals and present several results using 2D range data. We first propose a learning(More)
People typically move and act under the constraints of an environment , making human behavior strongly place-dependent. Motion patterns, the places and the rates at which people appear, disappear, walk or stand are not random but engendered by the environment. In this paper, we learn a non-homogeneous spatial Poisson process to spatially ground human(More)
The ability to act in a socially-aware way is a key skill for robots that share a space with humans. In this paper we address the problem of socially-aware navigation among people that meets objective criteria such as travel time or path length as well as subjective criteria such as social comfort. Opposed to model-based approaches typically taken in(More)
People detection and tracking is a key component for robots and autonomous vehicles in human environments. While prior work mainly employed image or 2D range data for this task, in this paper, we address the problem using 3D range data. In our approach, a top-down classifier selects hypotheses from a bottom-up detector, both based on sets of boosted(More)
For robots operating in real-world environments, the ability to deal with dynamic entities such as humans, animals, vehicles, or other robots is of fundamental importance. The variability of dynamic objects, however, is large in general, which makes it hard to manually design suitable models for their appearance and dynamics. In this paper, we present an(More)
— People tracking is an important yet challenging task for mobile robots operating in populated environments and interacting with humans. What makes this problem difficult is that human behavior is complex and hard to predict. However, motion of people , the rate at which people appear and where they appear are not random but strongly place-dependent and(More)
—Detecting and tracking people and groups of people is a key skill for robots in populated environments. In this paper, we address the problem of detecting and learning socio-spatial relations between individuals and to track their group formations. Opposed to related work, we track and reason about multiple social grouping hypotheses in a recursive way,(More)