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Kai Arras

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2 papers

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AAAI Conference 2014 Conference Paper

Schedule-Based Robotic Search for Multiple Residents in a Retirement Home Environment

Markus Schwenk
Tiago Vaquero
Goldie Nejat
Kai Arras

In this paper we address the planning problem of a robot searching for multiple residents in a retirement home in order to remind them of an upcoming multi-person recreational activity before a given deadline. We introduce a novel Multi-User Schedule Based (M-USB) Search approach which generates a high-level-plan to maximize the number of residents that are found within the given time frame. From the schedules of the residents, the layout of the retirement home environment as well as direct observations by the robot, we obtain spatio-temporal likelihood functions for the individual residents. The main contribution of our work is the development of a novel approach to compute a reward to find a search plan for the robot using: 1) the likelihood functions, 2) the availabilities of the residents, and 3) the order in which the residents should be found. Simulations were conducted on a floor of a real retirement home to compare our proposed M-USB Search approach to a Weighted Informed Walk and a Random Walk. Our results show that the proposed M-USB Search finds residents in a shorter amount of time by visiting fewer rooms when compared to the other approaches.

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AAAI Conference 2010 Conference Paper

A Layered Approach to People Detection in 3D Range Data

Luciano Spinello
Kai Arras
Rudolph Triebel
Roland Siegwart

People tracking is a key technology for autonomous systems, intelligent cars and social robots operating in populated environments. What makes the task di cult is that the appearance of humans in range data can change drastically as a function of body pose, distance to the sensor, self-occlusion and occlusion by other objects. In this paper we propose a novel approach to pedestrian detection in 3D range data based on supervised learning techniques to create a bank of classifiers for di erent height levels of the human body. In particular, our approach applies AdaBoost to train a strong classifier from geometrical and statistical features of groups of neighboring points at the same height. In a second step, the AdaBoost classifiers mutually enforce their evidence across di erent heights by voting into a continuous space. Pedestrians are finally found e ciently by mean-shift search for local maxima in the voting space. Experimental results carried out with 3D laser range data illustrate the robustness and e ciency of our approach even in cluttered urban environments. The learned people detector reaches a classification rate up to 96% from a single 3D scan.

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