Robot localization and mapping problem with unknown noise characteristics

金沢大学理工研究域Background: The retrochiasmatic region is one of the most challenging areas to surgically expose. The authors evaluated the transcrusal approach, which involves removal of the superior and posterior semicircular canal from the ampulla to the common crus, to expose the retrochiasmatic region and compared it with the retrolabyrinthine approach, both of which are a variation of the posterior petrosal approach with hearing preservation, with a special emphasis on the influence of temporal lobe retraction. Methods: Six sides of silicone-injected cadaveric heads were dissected using two approaches: the transcrusal approach and the retrolabyrinthine approach. For each craniotomy, 3 exposure parameters in the retrochiasmatic region were measured: (1) horizontal distance, (2) vertical distance, and (3) triangular area of exposure, at three different levels of temporal lobe retractions: 0, 5, and 10 mm of retraction from the level of the tentorial incisura. Results: Without temporal lobe retraction, only the transcrusal and not the retrolabyrinthine approach provided a direct exposure of the retrochiasmatic region, especially in the horizontal distance (p < 0.001). At all levels of temporal lobe retraction, the transcrusal approach provided greater exposure in the horizontal and vertical distances and in the area of exposure. Nonetheless, in the horizontal distance, the difference between the transcrusal and retrolabyrinthine approaches decreased along with increased temporal lobe retraction, and almost no difference was obtained at 10 mm of retraction. Conclusions: Posterior petrosal approaches can provide an excellent exposure of the retrochiasmatic region. Of these two approaches, namely, transcrusal and retrolabyrinthine with hearing preservation, the transcrusal approach offers greater exposure than the retrolabyrinthine approach. The beneficial effect of partial labyrinthectomy of the transcrusal approach to the retrochiasmatic region is accentuated in the exposure of the horizontal distance with less temporal lobe retraction. © 2010 Springer-Verlag

Mobile robot localization using a Kalman filter and relative bearing measurements to known landmarks

This paper discusses mobile robot localization using a single, fixed camera that is capable of detecting predefined landmarks in the environment. For each visible landmark, the camera provides a relative bearing but not a relative range. This research represents work toward an inexpensive sensor that could be added to a mobile robot in order to provide more accurate estimates of the robot\u27s location. It uses the Kalman filter as a framework, which is a proven method for incorporating sensor data into navigation problems. In the simulations presented later, it is assumed that the filter can perform accurate feature recognition. In the experimental setup, however, a webcam and an open source library are used to recognize and track bearing to a set of unique markers. Although this research requires that the landmark locations be known, in contrast to research in simultaneous localization and mapping, the results are still useful in an industrial setting where placing known landmarks would be acceptable

An Adaptive Augmented Vision-based Ellipsoidal SLAM for Indoor Environments

In this paper, the problem of Simultaneous Localization And Mapping (SLAM) is addressed via a novel augmented landmark vision-based ellipsoidal SLAM. The algorithm is implemented on a NAO humanoid robot and is tested in an indoor environment. The main feature of the system is the implementation of SLAM with a monocular vision system. Distinguished landmarks referred to as NAOmarks are employed to localize the robot via its monocular vision system. We henceforth introduce the notion of robotic augmented reality (RAR) and present a monocular Extended Kalman Filter (EKF)/ellipsoidal SLAM in order to improve the performance and alleviate the computational effort, to provide landmark identification, and to simplify the data association problem. The proposed SLAM algorithm is implemented in real-time to further calibrate the ellipsoidal SLAM parameters, noise bounding, and to improve its overall accuracy. The augmented EKF/ellipsoidal SLAM algorithms are compared with the regular EKF/ellipsoidal SLAM methods and the merits of each algorithm is also discussed in the paper. The real-time experimental and simulation studies suggest that the adaptive augmented ellipsoidal SLAM is more accurate than the conventional EKF/ellipsoidal SLAMs

DECENTRALIZED TRAFFIC MANAGEMENT OF MULTI-AGENT SYSTEMS

Autonomous agents and multi-agent systems (MASs) represent one of the most exciting and challenging areas of robotics research during the last two decades. In recent years, they have been proposed for several applications, such as telecommunications, air traffic mangement, planetary exploration, surveillance etc.. MASs offer many potential advantages with respect to single-agent systems such as speedup in task execution, robustness with respect to failure of one or more agents, scalability and modularity. On the other hand, MASs introduce challenging issues such as the handling of distributed information data, the coordination among agents, the choice of the control framework and of communication protocols. This thesis investigates some problems that arise in the management of MASs. More specifically it investigates problems of designing decentralized control schemes to manage collections of vehicles cooperating to reach common goals, while simultaneously avoiding collisions. An existing decentralized policy for collisions avoidance, already proved safe for a system with three agents, has been extended up to five agents. A new decentralized policy, the Generalized Roundabout Policy, has been designed and its properties analyzed. Specifically safety and liveness properties have been studied. The first one has been proved formally, while the second has been addressed by means of probabilistic approaches. Moreover, it is addressed the problem of optimization of autonomous robotic exploration. The problem is clearly of great relevance to many tasks, such as e.g. surveillance or exploration. However, it is in general a difficult problem, as several quantities have to be traded off, such as the expected gain in map information, the time and energy it takes to gain this information, the possible loss of pose information along the way, and so on. Finally, software and hardware simulation tools have been developed for the analysis and the verification of the decentralized control policies. Such instruments are particularly useful for the verification of multi-agent systems which could be overwhelmingly complex to be addressed purely by a theoretical approach

Set membership localization of mobile robots via angle measurements

This paper addresses the localization problem for a mobile robot navigating in an unstructured outdoor environment. A new technique is introduced, for computing an estimate of the position of the robot and the related uncertainty region, in the presence of visual angle measurements affected by bounded errors. The proposed set membership estimation procedure exploits the structure of the static set estimator, to solve recursively the dynamic localization problem

Placement and motion planning algorithms for robotic sensing systems

University of Minnesota Ph.D. dissertation. October 2014. Major: Computer Science. Advisor: Prof. Ibrahim Volkan Isler. I computer file (PDF); xxiii, 226 pages.Recent technological advances are making it possible to build teams of sensors and robots that can sense data from hard-to-reach places at unprecedented spatio-temporal scales. Robotic sensing systems hold the potential to revolutionize a diverse collection of applications such as agriculture, environmental monitoring, climate studies, security and surveillance in the near future. In order to make full use of this technology, it is crucial to complement it with efficient algorithms that plan for the sensing in these systems. In this dissertation, we develop new sensor planning algorithms and present prototype robotic sensing systems.In the first part of this dissertation, we study two problems on placing stationary sensors to cover an environment. Our objective is to place the fewest number of sensors required to ensure that every point in the environment is covered. In the first problem, we say a point is covered if it is seen by sensors from all orientations. The environment is represented as a polygon and the sensors are modeled as omnidirectional cameras. Our formulation, which builds on the well-known art gallery problem, is motivated by practical applications such as visual inspection and video-conferencing where seeing objects from all sides is crucial. In the second problem, we study how to deploy bearing sensors in order to localize a target in the environment. The sensors measure noisy bearings towards the target which can be combined to localize the target. The uncertainty in localization is a function of the placement of the sensors relative to the target. For both problems we present (i) lower bounds on the number of sensors required for an optimal algorithm, and (ii) algorithms to place at most a constant times the optimal number of sensors. In the second part of this dissertation, we study motion planning problems for mobile sensors. We start by investigating how to plan the motion of a team of aerial robots tasked with tracking targets that are moving on the ground. We then study various coverage problems that arise in two environmental monitoring applications: using robotic boats to monitor radio-tagged invasive fish in lakes, and using ground and aerial robots for data collection in precision agriculture. We formulate the coverage problems based on constraints observed in practice. We also present the design of prototype robotic systems for these applications. In the final problem, we investigate how to optimize the low-level motion of the robots to minimize their energy consumption and extend the system lifetime.This dissertation makes progress towards building robotic sensing systems along two directions. We present algorithms with strong theoretical performance guarantees, often by proving that our algorithms are optimal or that their costs are at most a constant factor away from the optimal values. We also demonstrate the feasibility and applicability of our results through system implementation and with results from simulations and extensive field experiments