A comparison of line extraction algorithms using 2D range data for indoor mobile robotics

This paper presents an experimental evaluation of different line extraction algorithms applied to 2D laser scans for indoor environments. Six popular algorithms in mobile robotics and computer vision are selected and tested. Real scan data collected from two office environments by using different platforms are used in the experiments in order to evaluate the algorithms. Several comparison criteria are proposed and discussed to highlight the advantages and drawbacks of each algorithm, including speed, complexity, correctness and precision. The results of the algorithms are compared with ground truth using standard statistical methods. An extended case study is performed to further evaluate the algorithms in a SLAM applicatio

Measuring social dynamics in a massive multiplayer online game

Quantification of human group-behavior has so far defied an empirical, falsifiable approach. This is due to tremendous difficulties in data acquisition of social systems. Massive multiplayer online games (MMOG) provide a fascinating new way of observing hundreds of thousands of simultaneously socially interacting individuals engaged in virtual economic activities. We have compiled a data set consisting of practically all actions of all players over a period of three years from a MMOG played by 300,000 people. This large-scale data set of a socio-economic unit contains all social and economic data from a single and coherent source. Players have to generate a virtual income through economic activities to `survive' and are typically engaged in a multitude of social activities offered within the game. Our analysis of high-frequency log files focuses on three types of social networks, and tests a series of social-dynamics hypotheses. In particular we study the structure and dynamics of friend-, enemy- and communication networks. We find striking differences in topological structure between positive (friend) and negative (enemy) tie networks. All networks confirm the recently observed phenomenon of network densification. We propose two approximate social laws in communication networks, the first expressing betweenness centrality as the inverse square of the overlap, the second relating communication strength to the cube of the overlap. These empirical laws provide strong quantitative evidence for the Weak ties hypothesis of Granovetter. Further, the analysis of triad significance profiles validates well-established assertions from social balance theory. We find overrepresentation (underrepresentation) of complete (incomplete) triads in networks of positive ties, and vice versa for networks of negative ties...Comment: 23 pages 19 figure

Initiating private-collective innovation: The fragility of knowledge sharing

Incentives to innovate are a central element of innovation theory. In the private-investment model, innovators privately fund innovation and then use intellectual property protection mechanisms to appropriate returns from these investments. In the collective-action model, public subsidy funds public goods innovations, characterized by non-rivalry and non-exclusivity in using these innovations. Recently, these models have been compounded in the private-collective innovation model where innovators privately fund public goods innovations. Private-collective innovation is illustrated in the case of open source software development. This paper contributes to the work on this model by investigating incentives that motivate innovators to share their knowledge in an initial situation, before there is a community to support the innovation process. We use game theory to predict knowledge sharing behavior in private-collective innovation, and test these predictions in a laboratory setting. The results show that knowledge sharing is a coordination game with multiple equilibria, reflecting the fragility of knowledge sharing between innovators with conflicting interests. The experimental results demonstrate important asymmetries in the fragility of knowledge sharing and, in some situations, more knowledge sharing than theoretically predicted. A behavioral analysis suggests that knowledge sharing in private-collective innovation is not only affected by material incentives, but also by social preferences such as fairness. The results offer general insights into the relationship between incentives and knowledge sharing and contribute to a better understanding of the initiation of private-collective innovation

The periphery on stage: The intra-organizational dynamics in online communities of creation

This paper theorizes the intra-organizational dynamics of online communities of creation such as Free and Open Source software projects. It describes the role of the participants at the peripheries of these online communities and analyze how the division of labor among peripheral and core members is handled. The paper further demonstrates that this mode of labor division is possible only if the periphery is able to acquire and absorb the standards associated with the developers' activities, described here as a social practice. We describe how the propagation of such standards takes place through non-material artifacts such as code and virtual discussions. We show that because of the capacity of these artifacts to effectively disseminate the standards of a social practice, such standards can be transferred not only face to face, but also asynchronously, asymmetrically and openly

Incremental Object Part Detection with a Range Camera

This report presents an object part detection method using a par- ticle lter. The method is adapted to a range camera that provides 3D information with a high data rate. However, the data is aected by considerable measurement noise and distortion. Thus, the range data is quantized to cope more eciently with the high data vol- ume and segmented into primitive parts with morphological oper- ators to assure processing speed. Measurement noise, outliers and segmentation errors are handled with a particle lter used here as a soft decision tree to detect object parts over several frames

Results on range image segmentation for service robots

This report presents an experimental evaluation of a plane extraction method using various line extraction algorithms. Four different algorithms are chosen, which are well known in mobile robotics and computer vision. Experiments are performed on two sets of 25 range images either obtained by simulation or acquired by a proprietary 3D laser scanner. The segmentation outcome of the simulated range images is measured in terms of an average segment classification ratio. Moreover, the speed of the method is measured to conclude on the suitability for service robot applications

R.: Incremental Object Part Detection with a Range Camera

This report presents an object part detection method using a par- ticle lter. The method is adapted to a range camera that provides 3D information with a high data rate. However, the data is aected by considerable measurement noise and distortion. Thus, the range data is quantized to cope more eciently with the high data vol- ume and segmented into primitive parts with morphological oper- ators to assure processing speed. Measurement noise, outliers and segmentation errors are handled with a particle lter used here as a soft decision tree to detect object parts over several frames

Results on range image segmentation for service robots

This paper presents an experimental evaluation of a plane extraction method using various line extraction algorithms. Four different algorithms are chosen, which are well known in mobile robotics and computer vision. Experiments are performed on two sets of 25 range images either obtained by simulation or acquired by a proprietary 3D laser scanner. The performance of the range image segmentation is measured in terms of an average segment classification ratio. Moreover, the speed of the method is measured to conclude on the suitability for service robot applications.