FiFi – Steuerung eines FTF durch Gesten- und Personenerkennung

Um den manuellen Transport in der Intralogistik zu erleichtern, wurde ein Fahrerloses Transportfahrzeug (FTF) entwickelt, das berührungslos vom Bediener gesteuert wird. Die Steuerung erfolgt durch Gesten- und Personenerkennung basierend auf 3D-Daten der Umgebung. Das Paper beschreibt sowohl Zielsetzung, Betriebsarten und Anwendungsmöglichkeiten als auch das Steuerungskonzept der berührungslosen Steuerung und die technische Umsetzung der Plattform. Erste Experimente bestätigen, dass ein Roboter basierend auf 3D-Daten gesteuert werden kann. Verbesserungsmöglichkeiten in der Robustheit werden aufgezeigt.

Hands-Free Navigation in Immersive Environments for the Evaluation of the Effectiveness of Indoor Navigation Systems

While navigation systems for cars are in widespread use, only recently, indoor navigation systems based on smartphone apps became technically feasible. Hence tools in order to plan and evaluate particular designs of information provision are needed. Since tests in real infrastructures are costly and environmental conditions cannot be held constant, one must resort to virtual infrastructures. This paper presents the development of an environment for the support of the design of indoor navigation systems whose center piece consists in a hands-free navigation method using the Microsoft Kinect in the four-sided Definitely Affordable Virtual Environment (DAVE). Navigation controls using the user's gestures and postures as the input to the controls are designed and implemented. The installation of expensive and bulky hardware like treadmills is avoided while still giving the user a good impression of the distance she has traveled in virtual space. An advantage in comparison to approaches using a head mounted display is that the DAVE allows the users to interact with their smartphone. Thus the effects of different indoor navigation systems can be evaluated already in the planning phase using the resulting system

Estimating Gesture Accuracy in Motion-Based Health Games

This manuscript details a technique for estimating gesture accuracy within the context of motion-based health video games using the MICROSOFT KINECT. We created a physical therapy game that requires players to imitate clinically significant reference gestures. Player performance is represented by the degree of similarity between the performed and reference gestures and is quantified by collecting the Euler angles of the player's gestures, converting them to a three-dimensional vector, and comparing the magnitude between the vectors. Lower difference values represent greater gestural correspondence and therefore greater player performance. A group of thirty-one subjects was tested. Subjects achieved gestural correspondence sufficient to complete the game's objectives while also improving their ability to perform reference gestures accurately.

FiFi – Robuste Personenerkennung gestengesteuerter Fahrzeuge in der Intralogistik

Die Steuerung des Fahrerlosen Transportfahrzeuges „FiFi“ erfolgt berührungslos durch Gesten- und Personenerkennung basierend auf 3D-Daten der Umgebung. Die genutzten Verfahren zur Personenerkennung führen in einigen Fällen zur Falsch-Erkennung von Personen in Objekten. Das Paper beschreibt die Ursachen der Fehlerkennung und stellt die umgesetzten Lösungsansätze zur Vermeidung vor. Experimente bestätigen, dass die entwickelten Verfahren die Robustheit des Systems erhöhen.

Real-time depth camera tracking with CAD models and ICP

In recent years, depth cameras have been widely utilized in camera tracking for augmented and mixed reality. Many of the studies focus on the methods that generate the reference model simultaneously with the tracking and allow operation in unprepared environments. However, methods that rely on predefined CAD models have their advantages. In such methods, the measurement errors are not accumulated to the model, they are tolerant to inaccurate initialization, and the tracking is always performed directly in reference model's coordinate system. In this paper, we present a method for tracking a depth camera with existing CAD models and the Iterative Closest Point (ICP) algorithm. In our approach, we render the CAD model using the latest pose estimate and construct a point cloud from the corresponding depth map. We construct another point cloud from currently captured depth frame, and find the incremental change in the camera pose by aligning the point clouds. We utilize a GPGPU-based implementation of the ICP which efficiently uses all the depth data in the process. The method runs in real-time, it is robust for outliers, and it does not require any preprocessing of the CAD models. We evaluated the approach using the Kinect depth sensor, and compared the results to a 2D edge-based method, to a depth-based SLAM method, and to the ground truth. The results show that the approach is more stable compared to the edge-based method and it suffers less from drift compared to the depth-based SLAM.