Cyrill Stachniss

Spatial Cognition is concerned with the acquisition, organization, utilization and revision of knowledge about spatial environments, be it real or abstract, human or machine. Research issues range from the investigation of human spatial cognition to mobile robot navigation. The goal of the SFB/TR 8 is to investigate the cognitive foundations for human-centered spatial assistance systems.

The SFB/TR 8 Spatial Cognition comprises 13 projects which are structured into the three research areas Reasoning, Action, and Interaction. Reasoning projects are concerned with internal and external representations of space and with inference processes using these representations. Action projects are concerned with the acquisition of information from spatial environments and with actions and behavior in these environments. Interaction projects are concerned with communication about space by means of language and maps.

Contemporary robots and other cognitive artifacts are not yet ready to autonomously operate in complex real world environments. One of the major reasons for this failure in creating cognitive situated systems is the difficulty in the handling of incomplete knowledge and uncertainty.

By taking up inspiration from the brains of mammals, including humans, the BACS project will investigate and apply Bayesian models and approaches in order to develop artificial cognitive systems that can carry out complex tasks in real world environments. The Bayesian approach will be used to model different levels of brain function within a coherent framework, from neural functions up to complex behaviors. The Bayesian models will be validated and adapted as necessary according to neuro-physiological data from rats and humans and through psychophysical experiments on humans. The Bayesian approach will also be used to develop four artificial cognitive systems concerned with (i) autonomous navigation, (ii) multi-modal perception and reconstruction of the environment, (iii) semantic facial motion tracking, and (iv) human body motion recognition and behavior analysis. The conducted research shall result in a consistent Bayesian framework offering enhanced tools for probabilistic reasoning in complex real world situations. The performance will be demonstrated through its applications to drive assistant systems and 3D mapping, both very complex real world tasks. BACS is an Integrated Project under the 6th Framework program of the European Commission running from January 2006 to February 2010.

RAWSEEDS - Robotics Advancements through Web-publishing of Sensorial and Elaborated Extensive Data Sets. The project is funded by the EU and aims to stimulate and support progress in autonomous robotics by providing a comprehensive, high-quality benchmarking toolkit.

The main goal of the EU project CoSy is to advance the science of cognitive systems through a multi-disciplinary investigation of requirements, design options and trade-offs for human-like, autonomous, integrated, physical (eg., robot) systems, including requirements for architectures, for forms of representation, for perceptual mechanisms, for learning, planning, reasoning and motivation, for action and communication.

The project aims at integrating leading edge technology in the field of service robotics and to develop an open, extensible system architecture. The project is funded by the German ministery of research.

The EU funded project WebFAIR addresses the marketing and promotion requirements of large commercial exhibitions by providing broad access to information, services and commodities exhibited at the event. Essentially, WebFAIR aims at providing the means to remote corporate and private users for active and personalised workplace exploration and information visualisation for commercial purposes.

CARMEN an open-source collection of software for mobile robot control. CARMEN is modular software designed to provide basic navigation primatives including: module communication infrastructure, base and sensor control, obstacle avoidance, localization, path planning, and mapping.

• Visit the CARMEN homepage
  
• Download the latest CARMEN release
  
• See the CARMEN Binary as well as source code/API documentation
  

The simultaneous localization and mapping (SLAM) problem has been intensively studied in the robotics community in the past. Different techniques have been proposed but only a few of them are available as implementations to the community. The goal of OpenSLAM.org is to provide a platform for SLAM researchers which gives them the possibility to publish and promote their algorithms.

GMapping is highly efficient Rao-Blackwellized particle filer to learn grid maps I developed together with Giorgio Grisetti. The project is hosted on www.openslam.org and the sourcecode is available.

TORO is an optimization approach for constraint-network. It provides a highly efficient, gradient descent-based error minimization procedure. In 2006, Olson et al. presented a novel approach to solve the graph-based SLAM problem by applying stochastic gradient descent to minimize the error introduced by constraints. TORO is an extension of Olson's algorithm. It applies a tree parameterization of the nodes in the graph that significantly improves the performance and enables a robot to cope with arbitrary network topologies. The latter allows us to bound the complexity of the algorithm to the size of the mapped area and not to the length of the trajectory.

This project is a cooperation between the ETH Zurich, the EPLF in Lausanne, and the University of Freiburg. The goal is to build an automous (Smart) car.

I am acutually not an official contributor of Radish, however, I like it and submitted a series of robotic datasets into that repository.

The Robotics Data Set Repository provides a collection of standard robotics data sets. You will find there:

• Logs of odometry, laser and sonar data taken from real robots.
• Logs of all sorts of sensor data taken from simulated robots.
• Environment maps generated by robots.
• Environment maps generated by hand (i.e., re-touched floor-plans).

By making these data sets available to the community, Radish aims to facilitate the development, evaluation and comparison of robotics algorithms. While the current focus is clearly on localization and mapping, Radish will ultimately expand to reflect the interests of the broader robotics community.

Radish is a community effort. Researchers are invited to download and make use of the data sets, and, in return, to make their own contributions to the repository.