全球定位系統 (GPS) 是目前最受歡迎的導航技術，其最主要的缺點是容易受到衛星訊號不足影響導致無法定位或定位精度差。慣性導航系統(INS)雖然可以提供不間斷的定位與定向結果，誤差卻會隨時間累積。本文提出了一種後處理的演算架構，利用地圖的資訊來改善車載GPS/INS導航結果。當GPS遭遇訊號失鎖時，由地圖提供載具行進的方向，輪速器提供速度，可以估計載具的位置，再由數值高程模型(DEM)提供高程。測試結果顯示此演算架構可以大幅改善衛星訊號失鎖期間的GPS/INS定位與定向結果。

Currently, the Global Positioning System (GPS) is the most prevalent navigation method. However it suffers from signal blockage in places like urban canyons. The Inertial Navigation System (INS), on the other hand, provides position, orientation, and velocity information without external reference. But errors in INS accumulate over time, especially for low cost MEMS devices. This paper presents a post-processing method to improve GPS/INS navigation results by using auxiliary map information. When GPS temporally loses a signal, location can be estimated based on the map information providing driving direction and odometers, which can be used to compute velocities, as well as a digital elevation model (DEM) giving accurate height information of the estimated location. This paper presents a procedure to handle these three data sources and then integrate them into the Kalman filter algorithm. By approximating locations during signal blockage GPS/INS navigation solutions are significantly improved.

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