Johannes Pellenz

RoboCup Rescue Robot and Simulation Leagues

The RoboCup Rescue Robot and Simulation competitions have been held since 2000. The experience gained during these competitions has increased the maturity level of the field, which allowed deployin ...

Terrain drivability analysis in 3D laser range data for autonomous robot navigation in unstructured environments

Three-dimensional laser range finders provide autonomous systems with vast amounts of information. However, autonomous robots navigating in unstructured environments are usually not interested in every geometric detail of their surroundings. Instead, they require real-time information about the location of obstacles and the condition of drivable areas.In this paper, we first present grid-based algorithms for classifying regions as either drivable or not. In a subsequent step, drivable regions are further examined using a novel algorithm which determines the local terrain roughness. This information can be used by a path planning algorithm to decide whether to prefer a rough, muddy area, or a plain street, which would not be possible using binary drivability information only.

Real-time 3D mapping of rough terrain: A field report from Disaster City

Mobile systems for mapping and terrain classification are often tested on datasets of intact environments only. The behavior of the algorithms in unstructured environments is mostly unknown. In safety, security and rescue environments, the robots have to handle much rougher terrain. Therefore, there is a need for 3D test data that also contains disaster scenarios. During the Response Robot Evaluation Exercise in March 2010 in Disaster City, College Station, Texas (USA), a comprehensive dataset was recorded containing the data of a 3D laser range finder, a GPS receiver, an IMU and a color camera. We tested our algorithms (for terrain classification and 3D mapping) with the dataset, and will make the data available to give other researchers the chance to do the same. We believe that this captured data of this well known location provides a valuable dataset for the USAR robotics community, increasing chances of getting more comparable results.

Advancing the State of Urban Search and Rescue Robotics Through the RoboCupRescue Robot League Competition

The RoboCupRescue Robot League is an international competition that has grown to be an effective driver for the dissemination of solutions to the challenges posed by Urban Search and Rescue Robotics and accelerated the development of the performance standards that are crucial to widespread effective deployment of robotic systems for these applications. In this paper, we will discuss how this competition has come to be more than simply a venue where teams compete to find a champion and is now “A League of Teams with one goal: to Develop and Demonstrate Advanced Robotic Capabilities for Emergency Responders.”

Using competitions to advance the development of standard test methods for response robots

Competitions are an effective aid to the development and dissemination of standard test methods, especially in rapidly developing, fields with a wide variety of requirements and capabilities such as Urban Search and Rescue robotics. By exposing the development process to highly developmental systems that push the boundaries of current capabilities, it is possible to gain an insight into how the test methods will respond to the robots of the future. The competition setting also allows for the rapid iterative refinement of the test methods and apparatuses in response to new developments. For the research community, introducing the concepts behind the test methods at the research and development stage can also help to guide their work towards the operationally relevant requirements embodied by the test methods and apparatuses. This also aids in the dissemination of the test methods themselves as teams fabricate them in their own laboratories and re-use them in work outside the competition. In this paper, we discuss how international competitions, and in particular the RoboCupRescue Robot League competition, have played a crucial role in the development of standard test metrics for response robots as part of the ASTM International Committee of Homeland Security Applications; Operational Equipment; Robots (E54.08.01). We will also discuss how the competition has helped to drive a vibrant robot developer community towards solutions that are relevant to first responders.

Hyperspectral Imaging or Victim Detection with Rescue Robots

The main task of rescue robots is to locate victims after a disaster such as an earthquake. For this task sensor data is used to localize the robots in their environment, build maps, and mark the victims in the maps. Usually, thermal and color cameras, monitored by a human operator, are used for the detection. Hyperspectral imaging techniques are today used for industrial tasks such as quality control, fast material sorting or food analysis. This paper proposes a new approach for the victim detection in rescue environments, based on hyperspectral imaging in the near infrared spectral domain. This technique involves a simultaneous recording of spatial and spectral information. Different materials can be distinguished when the spectra are analyzed. The result of the experiments show that the spectra of skin are very characteristic and that even under the impact of ash layers the spectral similarity remains very high. Thus our approach can be used for rescue robots to find human bodies autonomously, where other techniques such as color or thermal image analysis would fail.

Using a fiducial map metric for assessing map quality in the context of RoboCup Rescue

Mapping is an important task for mobile robots in general and for Safety, Security, and Rescue Robotics (SSRR) in particular. It is hence one core aspect which is evaluated in the RoboCup Rescue league. But assessing the quality of maps in a simple and efficient way is not trivial, especially if no detailed, complete ground truth data of the environment is available. A new approach on map evaluation is presented here. It makes use of artificial objects placed in the environment named “fiducials”. Using the known ground-truth positions and the positions of the fiducials identified in the map, a number of quality attributes can be assigned to that map. Depending on the application domain those attributes can weighed to compute a final score. Results are presented that are based on using this method during the RoboCup Rescue competition 2010 in Singapore where maps were generated by different teams in an maze populated with fiducials. Those maps are evaluated here and compared to a human judgment, showing the effectiveness of the fiducial approach.

Stable mapping using a hyper particle filter

Often Particle Filters are used to solve the SLAM (Simultaneous Localization and Mapping) problem in robotics: The particles represent the possible poses of the robot, and their weight is determined by checking if the sensor readings are consistent with the so far acquired map. Mostly a single map is maintained during the exploration, and only with Rao-Blackwellized Particle Filters each particle carries its own map. In this contribution, we propose a Hyper Particle Filter (HPF) – a Particle Filter of Particle Filters – for solving the SLAM problem in unstructured environments. Each particle of the HPF contains a standard Particle Filter (with a map and a set particles, that model the belief of the robot pose in this particular map). To measure the weight of a particle in the HPF, we developed two map quality measures that can be calculated automatically and do not rely on a ground truth map: The first map quality measure determines the contrast of the occupancy map. If the map has a high contrast, it is likely that the pose of the robot was always determined correctly before the map was updated, which finally leads to an overall consistent map. The second map quality measure determines the distribution of the orientation of wall pixels calculated by the Sobel operator. Using the model of a rectangular overall structure, slight but systematic errors in the map can be detected. Using the two measures, broken maps can automatically be detected. The corresponding particle is then more likely to be replaced by a particle with a better map within the HPF. We implemented the approach on our robot “Robbie 12”, which will be used in the RoboCup Rescue league in 2009. We tested the HPF using the log files from last years RoboCup Rescue autonomy final, and with new data of a larger building. The quality of the generated maps outperformed our last years (league’s best) maps. With the data acquired in the larger structure, Robbie was able to close loops in the map. Due to a highly efficient implementation, the algorithm still runs online during the autonomous exploration.

Community-driven development of standard software modules for search and rescue robots

The main goal of the paper is to continuously enlarge the set of software building blocks that can be reused in the search and rescue domain.

The RoboCupRescue robot league: Guiding robots towards fieldable capabilities

The RoboCupRescue Robot League is an international competition where teams from all over the world compete against an arena that allows them to demonstrate their advanced robotic capabilities for emergency response applications. The league is also a community that works together to advance the state-of-the-art towards improving performance and the standards that help quantify this performance. In this paper, we present the current state of the competition, its links to the wider standardization process and how it is guiding robots towards fieldable capabilities.