Hakan Karaoguz

RGB-D based place representation in topological maps

With the recent developments in sensor technology including Microsoft Kinect, it has now become much easier to augment visual data with three-dimensional depth information. In this paper, we propose a new approach to RGB-D based topological place representation—building on bubble space. While bubble space representation is in principle transparent to the type and number of sensory inputs employed, practically, this has been only verified with visual data that are acquired either via a two degrees of freedom camera head or an omnidirectional camera. The primary contribution of this paper is of practical nature in this perspective. We show that bubble space representation can easily be used to combine RGB and depth data while affording acceptable recognition performance even with limited field of view sensing and simple features.

Reliable topological place detection in bubble space

This paper introduces a novel approach to topo-logical place detection. The approach is based on previously proposed bubble space representation - where all sensory features and their relative S2- geometry are encoded in a manner that is implicitly dependent on robot pose. Its novelty is that ensuring sensory data reliability is integrated with place detection. This is achieved via checking for informativeness, coherence and plenitude using only the bubble space representation of the incoming sensory data. The stringency of these checks is controllable via a set of associated parameters. Experimental results with benchmark datasets indicate correct detection rates comparable to state-of-the-art approaches in place detection. Furthermore, the detected places can then be immediately used to generate the nodes in topological maps.

Design and implementation of a hierarchical hybrid controller for holonomic robot formations

Multi-robot coordination is one of the hot topics of todays robotics research. In our case, our main concern is to build up a small sized (SSL) robot soccer team. Robot soccer requires effective and fast robot control and coordination in order to score goals while avoiding frequent collisions with opponent robots. In this paper, our multi-robot platform and current control techniques we use are discussed. Our robots have 4 wheel omnidirectional drive system, running with brushless DC motors. A camera which is 2 meters above the field provides vision feedback to identify and control the robots. Robot identification is carried out by recognizing colored patches on top of the robots. Several formation strategies are proposed and implemented in the setup using the holonomic robots we have built.

Topological place recognition based on long-term memory retrieval

Topological place recognition is related to the retrieval of previously learned places from long-term memory. In this paper, we consider this problem and present a novel approach - based on the previously proposed bubble descriptor semantic tree (BDST) memory model. In the proposed approach, the robot combines decision-making at each searched node of the BDST along with a BDST traversal strategy in order to find the most related previous knowledge. In case the robot is kidnapped or has no knowledge of where it is coming from, the traversal uses top-down depth-first search. If the robot has been navigating and knows where it is coming from, it uses this knowledge to initiate its search in an integrated bottom-up and top-down manner. The experimental results indicate that the proposed approach generally improves recognition performance significantly in comparison to purely top-down traversal.

Interactive, Collaborative Robots: Challenges and Opportunities

Robotic technology has transformed manufacturing industry ever since the first industrial robot was put in use in the beginning of the 60s. The challenge of developing flexible solutions where prod ...

Hierarchically self-organizing visual place memory

Graphical Abstract Abstract A hierarchically organized visual place memory enables a robot to associate with its respective knowledge efficiently. In this paper, we consider how this organization can be done by the robot on its own throughout its operation and introduce an approach that is based on the agglomerative method SLINK. The hierarchy is obtained from a single link cluster analysis that is carried out based on similarity in the appearance space. As such, the robot can incrementally incorporate the knowledge of places into its visual place memory over the long term. The resulting place memory has an order-invariant hierarchy that enables both storage and construction efficiency. Experimental results obtained under the guided operation of the robot demonstrate that the robot is able to organize its place knowledge and relate to it efficiently. This is followed by experimental results under autonomous operation in which the robot evolves its visual place memory completely on its own.