We developed a robust real time hand gesture based interaction system to effectively communicate with a mobile robot which can operate in an outdoor environment. The system enables the user to operate a mobile robot using hand gesture based commands. In particular the system offers direct on site interaction providing better perception of environment to the user. To overcome the illumination challenges in outdoors, the system operates on depth images. Processed depth images are given as input to a convolutional neural network which is trained to detect static hand gestures. The system is evaluated in real world experiments on a mobile robot to show the operational efficiency in outdoor environment

In this work we evaluate the performance of a trained people detector like Implicit Shape Models (ISM) on thermal images. We compare its performance against two baseline algorithms. One is a simple thresholding approach that detects high temperature blobs in the same thermal images, an approach which is commonly used. Additionally, we compare the results on thermal data to results of the same detector working on images from the visible spectrum that are taken from a camera mounted close to the thermal one. Experiments are conducted on both indoor and outdoor data. Outdoor data was recorded at different temperatures and with a moving robot. The conclusion of this paper is that for indoor usage under normal circumstances simple blob detectors are sufficient. But outdoors a trained people detector outperforms the blob detector by far. Also, the results of the blob detector get much worse on warmer days which hints in the direction that blob detectors will also fail during unusual circumstances indoors, like during a fire. The use of thermal images helps the ISM detector to distinguish people from background clutter and, therefore, the performance of the detector is better on thermal images than on images from the visible spectrum showing the same scene

In this article, we present a component-based visual tracker for mobile platforms with an application to person tracking. The core of the technique is a componentbased descriptor that captures the structure and appearance of a target in a flexible way. This descriptor can be learned quickly from a single training image and is easily adaptable to different objects. It is especially well suited to represent humans since they usually do not have a uniform appearance but, due to clothing, consist of different parts with different appearance. We show how this component-based descriptor can be integrated into a visual tracker based on the well known Condensation algorithm. Several person tracking experiments carried out with a mobile robot in different laboratory environments show that the system is able to follow people autonomously and to distinguish individuals. We furthermore illustrate the advantage of our approach compared to other tracking methods.

In this paper we propose an integrated hard- and software system for autonomous exploration and mapping of dilapidated buildings. The system is based on well understood approaches towards SLAM and exploration. Because of the real world nature of the application, a fragile wireless connection and difficult obstacles like staircases were taken into account during system design. Additionally, a drop-off system for Wi-Fi relays was introduced in order to increase the communication range. Further, a homing function is added to safely explore beyond radio coverage. Negative obstacles are handled using a tilted laserscanner. A semi-automatic stair climbing mechanism allows to travel between floors. The system is interconnected with a control station, giving an operator a fine grained control over the mission without disturbing the exploration and mapping task. The system was successfully tested at the Eurathlon 2013 robotics competition under real world conditions

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