مورد إلكتروني
Maximum Likelihood Fusion Model
| العنوان: | Maximum Likelihood Fusion Model |
|---|---|
| المؤلفون: | CORNELL UNIV ITHACA NY, Jones, Brandon M |
| المصدر: | DTIC |
| بيانات النشر: | 2014-08-09 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | In the absence of a global frame of reference, the ability to fuse data collected by multiple mobile agents that operate in separate coordinate systems is critical for enabling autonomy in multi-agent navigation and perception systems. Of particular interest is the ability to fuse rigid body metric environment models in order to construct a global model from the data collected by each agent. This thesis presents a data fusion approach for combining Gaussian metric models of an environment constructed by multiple agents that operate outside of a global reference frame. Common landmarks are combined using a nonlinear least squares approximation, which yields an exact solution under the assumption of isotropic covariance. Rigid body transform parameters and common landmarks are found using a hypergraph registration approach. The approach demonstrates a robustness to outliers in registration by incorporating unit quaternions to reject outliers on a unit sphere. The performance of the approach is evaluated using experimental benchmark datasets collected in natural and semi-structured environments with camera and laser sensors. |
| مصطلحات الفهرس: | Operations Research, Cybernetics, DATA FUSION, MULTIAGENT SYSTEMS, AUTONOMOUS NAVIGATION, COGNITION, COLLABORATIVE TECHNIQUES, COMPUTER COMMUNICATIONS, COVARIANCE, GAUSSIAN NOISE, ISOTROPISM, KALMAN FILTERING, LEAST SQUARES METHOD, MAXIMUM LIKELIHOOD ESTIMATION, MONTE CARLO METHOD, MULTISENSORS, NONLINEAR ANALYSIS, PERFORMANCE(ENGINEERING), ROBOTICS, SIGNAL PROCESSING, STOCHASTIC PROCESSES, THESES, VISUAL PERCEPTION, DATA ASSOCIATION, HYPOTHESIS TESTING, MOBILE ROBOT NAVIGATION, Text |
| URL: | |
| الإتاحة: | Open access content. Open access content Approved for public release; distribution is unlimited. |
| ملاحظة: | text/html English |
| أرقام أخرى: | DTICE ADA617040 913598930 |
| المصدر المساهم: | From OAIster®, provided by the OCLC Cooperative. |
| رقم الأكسشن: | edsoai.ocn913598930 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |