In order to increase the robustness of localisation and victim detection in low visibility situations it is necessary to fuse several sensors. The most common sensor used in robotics is the 2D laser scanner which delivers distance measurements. In combination with a camera the gained information can be supported by visual information about the environment. Thermal cameras are ideal for finding objects with a certain temperature, but they do not deliver distance information. The difficulty in fusing these two sensors is, that a correspondence between each distance measurement and its corresponding pixel within the thermal image needs to be found. As the laser scanner only displays one plane, this is not an intuitive task. A special triangular calibration target, covering all six degrees of freedom and being visible for both sensors, was developed. In the end the transformation between each laser scan point and its corresponding thermal image pixel is given. This allows for assigning every laser measurement within the field of view a corresponding thermal pixel. The final application will enable detection of human beings and display the distance required to reach them.

This paper describes the application of a weight estimation method for emergency patients in clinical environments. The approach applies established algorithms for point cloud processing and filtering to data from a low-cost, structured light sensor. A patient's volume is estimated on the basis of their visible front surface. The approach is currently being tested in the workflow of the emergency room at the Universitätsklinikum Erlangen, Germany. Preliminary results show the accuracy of the approach in relation to other conservative means of weight measurements, for example, by physicians and anthropometric measurements.

Time-of-flight cameras constitute a smart and fast technology for 3D perception but lack in measurement precision and robustness. The authors present a comprehensive approach for 3D environment mapping based on this technology. Imprecision of depth measurements are properly handled by calibration and application of several filters. Robust registration is performed by a novel extension to the Iterative Closest Point algorithm. Remaining registration errors are reduced by global relaxation after loop-closure and surface smoothing. A laboratory ground truth evaluation is provided as well as 3D mapping experiments in a larger indoor environment.

This work focuses on the relevance of visual attention in affordance-inspired robotics. Among all approaches in robotics related to Gibson's concept of affordances the dealing with attention cues is only rudimentary. We are introducing this concept within the perception layer of our affordance-inspired robotic framework. In this context we present a high-performance visual attention system handling invariants in the optical array. This layer builds the base of higher-sophisticated tasks, like a "curiosity drive" that helps a robotic agent to explore its environment. Our attention system derived from VOCUS utilizes the parallel design of the graphics processing unit (GPU) and reaches real-time performance for the processing of online video streams in VGA resolution on a single computer platform. GPU-VOCUS is currently the fastest known visual attention system running on standard personal computers.

Recently developed 3D time-of-flight cameras have an enormous potential for mobile robotic applications in particular for 3D mapping and 3D navigation tasks. But similar to passive visual sensors, this type of camera is very fragile to changing lighting conditions and environment dynamics, which make it not applicable for mobile robots. The video shows the performance of our solution to environment dynamics. The solution consist of calibration routines, integration time controller and an accuracy filter for the Swiss Ranger SR-2 camera on a mobile robot.

Recently developed 3D time-of-flight cameras have an enormous potential for mobile robotic applications in particular for mapping and navigation tasks. This paper presents a new approach for online adaptation of different camera parameters to environment dynamics. These adaptations allow the usage of state-of-the-art 3D cameras reliably in real world environments and enable capturing of 3D scenes with up to 30 frames per second. The performance of the approach is evaluated with respect to different robotic specific tasks in changing environments. A video, showing the performance of the approach, is available at (S. May, et al., 2006)