Accurate pose estimation plays an important role in solution of simultaneous localization and mapping (SLAM) problem, required for many robotic applications. This paper presents a new approach called R-SLAM, primarily to overcome systematic and non-systematic odometry errors which are generally caused by uneven floors, unexpected objects on the floor or wheel-slippage due to skidding or fast turns.The hybrid approach presented here combines the strengths of feature based and grid based methods to produce globally consistent high resolution maps within various types of environments.
參考文獻：M.Rostami Shahrbabak and H.Nezamabadi-pour, " A New Approach to Binary PSO Algorithm" 14th Iranian Conference on Electrical Engineering, may 2006.
Radio frequency spectrum is a scarce and critical natural resource that is utilized for
many services including surveillance, navigation, communication, and broadcast-
ing. Recent years have seen tremendous growth in the use of spectrum especially by
commercial cellular operators. Ubiquitous use of smartphones and tablets is one
of the reasons behind an all-time high utilization of spectrum. As a result, cellular
operators are experiencing a shortage of radio spectrum to meet bandwidth
demands of users. On the other hand, spectrum measurements have shown that
much spectrum not held by cellular operators is underutilized even in dense urban
areas. This has motivated shared access to spectrum by secondary systems with no
or minimal impact on incumbent systems. Spectrum sharing is a promising
approach to solve the problem of spectrum congestion as it allows cellular operators
access to more spectrum in order to satisfy the ever-growing bandwidth demands of
commercial users.
A modern power grid needs to become smarter in order to provide an affordable,
reliable, and sustainable supply of electricity. For these reasons, a smart grid is
necessary to manage and control the increasingly complex future grid. Certain
smart grid elements including renewable energy, storage, microgrid, consumer
choice, and smart appliances like electric vehicles increase uncertainty in both
supply and demand of electric power.
Modern power systems involve large amount of investment. An electric power
system comprises of generation, transmission, and distribution of electric energy.
Growth of power systems has led to very complex networks extended across large
areas. In such situations, the proper functioning of a modern power system is
heavily dependent upon the healthy operation of the transmission lines within it.
Transmission lines are used to transmit a huge amount of power over a long
distance. But as these lines are located in the open atmosphere, they are highly
affected by different types of abnormal conditions or faults.
Radio frequency identifi cation (RFID) is a modern wireless data transmission and
reception technique for applications including automatic identifi cation, asset track-
ing and security surveillance. As barcodes and other means of identifi cation and
asset tracking are inadequate for recent demands, RFID technology has attracted
interest for applications such as logistics, supply chain management, asset tracking
and security access control.
在信號的產生、傳輸、接收過程當中，必定會遭受外部環境擾動和內部設備噪聲的影響，為獲得需求信號或狀態的最有效估計，要排除無用干擾，這就叫做濾波。“濾波”的術語在無線電領域首先出現。由于隨機信號功率譜的確定性，有用信號和無用信號必定不同，從而可以根據其差異來設計濾波器。1960年，卡爾曼發表了用遞歸方法解決離散數據線性濾波問題的論文（A New Approach to Linear Filtering and Prediction Problems）。在這篇文章里，一種克服了維納濾波缺點的新方法被提出來，這就是我們今天稱之為卡爾曼濾波的方法。卡爾曼濾波應用廣泛且功能強大，它可以估計信號的過去和當前狀態，甚至能估計將來的狀態，即使并不知道模型的確切性質。其基本思想是：以最小均方誤差為最佳估計準則，采用信號與噪聲的狀態空間模型，利用前一時刻的估計值和當前時刻的觀測值來更新對狀態變量的估計，求出當前時刻的估計值，算法根據建立的系統方程和觀測方程對需要處理的信號做出滿足最小均方誤差的估計。
近來發現有些問題很多人都很感興趣。所以在這里希望能盡自己能力跟大家討論一些力所能及的算法。現在先討論一下卡爾曼濾波器，如果時間和能力允許，我還希望能夠寫寫其他的算法，例如遺傳算法，傅立葉變換，數字濾波，神經網絡，圖像處理等等。 因為這里不能寫復雜的數學公式，所以也只能形象的描述。希望如果哪位是這方面的專家，歡迎討論更正。 卡爾曼濾波器 – Kalman Filter 1． 什么是卡爾曼濾波器 （What is the Kalman Filter?） 在學習卡爾曼濾波器之前，首先看看為什么叫“卡爾曼”。跟其他著名的理論（例如傅立葉變換，泰勒級數等等）一樣，卡爾曼也是一個人的名字，而跟他們不同的是，他是個現代人！ 卡爾曼全名Rudolf Emil Kalman，匈牙利數學家，1930年出生于匈牙利首都布達佩斯。1953，1954年于麻省理工學院分別獲得電機工程學士及碩士學位。1957年于哥倫比亞大學獲得博士學位。我們現在要學習的卡爾曼濾波器，正是源于他的博士論文和1960年發表的論文《A New Approach to Linear Filtering and Prediction Problems》（線性濾波與預測問題的新方法）。如果對這編論文有興趣
