@article{DarmsFoelsterSchmidtetal.2010, author = {Darms, M. and F{\"o}lster, F. and Schmidt, Jochen and Fr{\"o}hlich, D. and Eckert, A.}, title = {Data Fusion Strategies in Advanced Driver Assistance Systems}, series = {SAE International Journal of Passenger Cars - Electronic and Electrical Systems}, journal = {SAE International Journal of Passenger Cars - Electronic and Electrical Systems}, year = {2010}, abstract = {Data fusion plays a central role in more and more automotive applications, especially for driver assistance systems. On the one hand the process of data fusion combines data and information to estimate or predict states of observed objects. On the other hand data fusion introduces abstraction layers for data description and allows building more flexible and modular systems.The data fusion process can be divided into a low-level processing (tracking and object discrimination) and a high level processing (situation assessment). High level processing becomes more and more the focus of current research as different assistance applications will be combined into one comprehensive assistance system. Different levels/strategies for data fusion can be distinguished: Fusion on raw data level, fusion on feature level and fusion on decision level. All fusion strategies can be found in current driver assistance implementations. The paper gives an overview of the different fusion strategies and shows their application in current driver assistance systems. For low level processing a raw data fusion approach in a stereo video system is described, as an example for feature level fusion the fusion of radar and camera data for tracking is explained. As an example for a high level fusion algorithm an approach for a situation assessment based on multiple sensors is given. The paper describes practical realizations of these examples and points out their potential to further increase traffic safety with reasonably low cost for the overall system.}, language = {en} } @inproceedings{WongYeapSchmidt2009, author = {Wong, C.K. and Yeap, W.K. and Schmidt, Jochen}, title = {Our Next Generation of Robotics Researchers? Teaching Robotics at Primary School Level.}, series = {Readings in Technology and Education: Proceedings of International Conference on Information Communication Technologies in Education}, booktitle = {Readings in Technology and Education: Proceedings of International Conference on Information Communication Technologies in Education}, year = {2009}, abstract = {In this paper, we present our experience in designing and teaching of our first robotics course for students at primary school level. The course was carried out over a comparatively short period of time, namely 6 weeks, 2 hours per week. In contrast to many other projects, we use robots that researchers used to conduct their research and discuss problems faced by these researchers. Thus, this is not a behavioural study but a hands-on learning experience for the students. The aim is to highlight the development of autonomous robots and artificial intelligence as well as to promote science and robotics in schools.}, language = {de} } @article{SchmidtNiemann2008, author = {Schmidt, Jochen and Niemann, H.}, title = {Data Selection for Hand-eye Calibration: A Vector Quantization Approach}, series = {The International Journal of Robotics Research}, journal = {The International Journal of Robotics Research}, year = {2008}, abstract = {This paper presents new vector quantization based methods for selecting well-suited data for hand-eye calibration from a given sequence of hand and eye movements. Data selection can improve the accuracy of classic hand-eye calibration, and make it possible in the first place in situations where the standard approach of manually selecting positions is inconvenient or even impossible, especially when using continuously recorded data. A variety of methods is proposed, which differ from each other in the dimensionality of the vector quantization compared to the degrees of freedom of the rotation representation, and how the rotation angle is incorporated. The performance of the proposed vector quantization based data selection methods is evaluated using data obtained from a manually moved optical tracking system (hand) and an endoscopic camera (eye).}, language = {en} } @incollection{YeapWongSchmidt2008, author = {Yeap, W.K. and Wong, C.K. and Schmidt, Jochen}, title = {Using a Mobile Robot to Test a Theory of Cognitive Mapping.}, series = {Robotics and Cognitive Approaches to Spatial Mapping, Springer Tracts in Advanced Robotics}, booktitle = {Robotics and Cognitive Approaches to Spatial Mapping, Springer Tracts in Advanced Robotics}, publisher = {Springer}, address = {Berlin}, publisher = {Technische Hochschule Rosenheim}, year = {2008}, abstract = {This paper describes using a mobile robot, equipped with some sonar sensors and an odometer, to test navigation through the use of a cognitive map. The robot explores an office environment, computes a cognitive map, which is a network of ASRs [36, 35], and attempts to find its way home. Ten trials were conducted and the robot found its way home each time. From four random positions in two trials, the robot estimated the home position relative to its current position reasonably accurately. Our robot does not solve the simultaneous localization and mapping problem and the map computed is fuzzy and inaccurate with much of the details missing. In each homeward journey, it computes a new cognitive map of the same part of the environment, as seen from the perspective of the homeward journey. We show how the robot uses distance information from both maps to find its way home.}, language = {en} } @inproceedings{SchmidtWongYeap2007, author = {Schmidt, Jochen and Wong, C.K. and Yeap, W.K.}, title = {Spatial Information Extraction for Cognitive Mapping with a Mobile Robot.}, series = {Conference on Spatial Information Theory: COSIT'07, Melbourne, Australia. Volume 4736 of Lecture Notes in Computer Science}, booktitle = {Conference on Spatial Information Theory: COSIT'07, Melbourne, Australia. Volume 4736 of Lecture Notes in Computer Science}, year = {2007}, abstract = {When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more powerful heuristics while lower animals have shown to develop a variety of methods that tend to utilize two key pieces of information, namely distance and orientation information. Their methods differ depending on how they sense their environment. Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting "cognitive map" be used for localization? In this paper, we present an approach using a mobile robot to generate a "cognitive map", the main focus being on experiments conducted in large spaces that the robot cannot apprehend at once due to the very limited range of its sensors. The robot computes a "cognitive map" and uses distance and orientation information for localization.}, language = {en} } @article{SchmidtWongYeap2007, author = {Schmidt, Jochen and Wong, C.K. and Yeap, W.K.}, title = {Localisation and Mapping with a Mobile Robot Using Sparse Range Data}, series = {Autonomous Robots and Agents, Studies in Computational Intelligence (SCI),}, journal = {Autonomous Robots and Agents, Studies in Computational Intelligence (SCI),}, publisher = {Springer}, year = {2007}, abstract = {We present an approach for indoor mapping and localisation using sparse range data, acquired by a mobile robot equipped with sonar sensors. The chapter consists of two main parts. First, a split and merge based method for dividing a given metric map into distinct regions is presented, thus creating a topological map in a metric framework. Spatial information extracted from this map is then used for self-localisation on the return home journey. The robot computes local confidence maps for two simple localisation strategies based on distance and relative orientation of regions. These local maps are then fused to produce overall confidence maps.}, language = {de} } @inproceedings{WongYeapSchmidt2007, author = {Wong, C.K. and Yeap, W.K. and Schmidt, Jochen}, title = {Using a Mobile Robot for Cognitive Mapping}, series = {International Joint Conference on Artificial Intelligence (IJCAI), pages 2243-2248, Hyderabad, India, 2007}, booktitle = {International Joint Conference on Artificial Intelligence (IJCAI), pages 2243-2248, Hyderabad, India, 2007}, year = {2007}, abstract = {When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more powerful heuristics while lower animals have shown to developa varietyof methodsthat tend to utilize two key pieces of information,namely distance and orientation information. Their methods differ depending on how they sense their environment. Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting "cognitive map" be used to return home? In this paper, we presented a novel approach using a mobile robot to do cognitive mapping. Our robot computes a "cognitive map" and uses distance and orientation information to find its way home. The process developed provides interesting insights into the nature of cognitive mapping and encourages us to use a mobile robot to do cognitive mapping in the future, as opposed to its popular use in robot mapping.}, language = {en} } @book{Schmidt2006, author = {Schmidt, Jochen}, title = {3-D Reconstruction and Stereo Self-Calibration for Augmented Reality}, publisher = {Logos}, address = {Berlin}, publisher = {Technische Hochschule Rosenheim}, year = {2006}, abstract = {The main focus of this work is the development of new methods for the self-calibration of a rigid stereo camera system. However, many of the algorithms introduced here have a wider impact, particularly in robot hand-eye calibration with all its different areas of application. Stereo self-calibration refers to the computation of the intrinsic and extrinsic parameters of a stereo rig using neither a priori knowledge on the movement of the rig nor on the geometry of the observed scene. The stereo parameters obtained by self-calibration, namely rotation and translation from left to right camera, are used for computing depth maps for both images, which are applied for rendering correctly occluded virtual objects into a real scene (Augmented Reality). The proposed methods were evaluated on real and synthetic data and compared to algorithms from the literature. In addition to a stereo rig, an optical tracking system with a camera mounted on an endoscope was calibrated without a calibration pattern using the proposed extended hand-eye calibration algorithm. The self-calibration methods developed in this work have a number of features, which make them easily applicable in practice: They rely on temporal feature tracking only, as this monocular tracking in a continuous image sequence is much easier than left-to-right tracking when the camera parameters are still unknown. Intrinsic and extrinsic camera parameters are computed during the self-calibration process, i.e., no calibration pattern is required. The proposed stereo self-calibration approach can also be used for extended hand-eye calibration, where the eye poses are obtained by structure-from-motion rather than from a calibration pattern. An inherent problem to hand-eye calibration is that it requires at least two general movements of the cameras in order to compute the rigid transformation. If the motion is not general enough, only a part of the parameters can be obtained, which would not be sufficient for computing depth maps. Therefore, a main part of this work discusses methods for data selection that increase the robustness of hand-eye calibration. Different new approaches are shown, the most successful ones being based on vector quantization. The data selection algorithms developed in this work can not only be used for stereo self-calibration, but also for classic robot hand-eye calibration, and they are independent of the actually used hand-eye calibration algorithm.}, language = {en} } @inproceedings{SchmidtWongYeap2006, author = {Schmidt, Jochen and Wong, C.K. and Yeap, W.K.}, title = {Mapping and Localisation with Sparse Range Data}, series = {International Conference on Autonomous Robots and Agents (ICARA), pages 497-502, Palmerston North, New Zealand}, booktitle = {International Conference on Autonomous Robots and Agents (ICARA), pages 497-502, Palmerston North, New Zealand}, year = {2006}, abstract = {We present an approach for indoor mapping and localization with a mobile robot using sparse range data, without the need for solving the SLAM problem. The paper consists of two main parts. First, a split and merge based method for dividing a given metric map into distinct regions is presented, thus creating a topological map in a metric framework. Spatial information extracted from this map is then used for self-localization. The robot computes local confidence maps for two simple localization strategies based on distance and relative orientation of regions. The local confidence maps are then fused using an approach adapted from computer vision to produce overall confidence maps. Experiments on data acquired by mobile robots equipped with sonar sensors are presented.}, language = {en} } @inproceedings{SchmidtWongYeap2006, author = {Schmidt, Jochen and Wong, C.K. and Yeap, W.K.}, title = {A Split \& Merge Approach to Metric-Topological Map-Building}, series = {International Conference on Pattern Recognition (ICPR), volume 3, pages 1069-1072, Hong Kong}, booktitle = {International Conference on Pattern Recognition (ICPR), volume 3, pages 1069-1072, Hong Kong}, year = {2006}, abstract = {We present a novel split and merge based method for dividing a given metric map into distinct regions, thus effectively creating a topological map on top of a metric one. The initial metric map is obtained from range data that are converted to a geometric map consisting of linear approximations of the indoor environment. The splitting is done using an objective function that computes the quality of a region, based on criteria such as the average region width (to distinguish big rooms from corridors) and overall direction (which accounts for sharp bends). A regularization term is used in order to avoid the formation of very small regions, which may originate from missing or unreliable sensor data. Experiments based on data acquired by a mobile robot equipped with sonar sensors are presented, which demonstrate the capabilities of the proposed method.}, language = {en} }