Multi-UAS Formation Recognition in Dynamic Environments
نویسندگان
چکیده
منابع مشابه
Place recognition in dynamic environments
We have developed a technique for place learning and place recognition in dynamic environments. Our technique associates evidence grids with places in the world and uses hill climbing to find the best alignment between current perceptions and learned evidence grids. We present results from five experiments performed using a real mobile robot in a real-world environment. These experiments measur...
متن کاملRobust Iris Recognition in Unconstrained Environments
A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by him/her. Iris recognition (IR) is known to be the most reliable and accurate biometric identification system. The iris recognition system (IRS) consists of an automatic segmentation mechanism which is based on the Hough transform (HT). This paper presents a robust IRS i...
متن کاملEffects of Moving Landmark’s Speed on Multi-Robot Simultaneous Localization and Mapping in Dynamic Environments
Even when simultaneous localization and mapping (SLAM) solutions have been broadly developed, the vast majority of them relate to a single robot performing measurements in static environments. Researches show that the performance of SLAM algorithms deteriorates under dynamic environments. In this paper, a multi-robot simultaneous localization and mapping (MR-SLAM) system is implemented within a...
متن کاملUsing Recognition in Multi-Attribute Decision Environments
An experiment examined the effect of ‘pure’ recognition — in the absence of concomitant evaluation — on inferences. In the first stage of the experiment, participants indicated whether they recognized a number of Italian and US cities. In the second stage, they decided which of two cities had the larger population. Crucially, names of the cities were not available in the second stage, but parti...
متن کاملMulti-band speech recognition in noisy environments
This paper presents a new approachfor multi-band based automatic speech recognition (ASR). Recent work by Bourlard and Hermansky suggests that multi-band ASR gives more accurate recognition, especially in noisy acoustic environments, by combining the likelihoods of different frequency bands. Here we evaluate this likelihood recombination (LC) approach to multi-band ASR, and propose an alternati...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Procedia Computer Science
سال: 2020
ISSN: 1877-0509
DOI: 10.1016/j.procs.2020.02.260