A pocket guide to indoor mapping

Abstract

Abstract-In this paper, we present a way to obtain accurate WLAN signal strength maps in indoor environments, without dedicated hardware, and without a time consuming and complicated training process. We need two contributions towards this end. First, we present a novel dead-reckoning technique, to gather accurate user motion estimates. This motion data is combined with information about the signal strength of access points of the wireless infrastructure. Our second contribution lies in the efficient integration of this complementary information into a system that allows for easy mapping and requires nothing but a smartphone. All the required data is gathered from people walking casually around in an area of interest while carrying a smartphone in their pocket.

A Robust Shadow Matching Algorithm for GNSS Positioning

“…The classic approach to deal with this problem is to perform information fusion of the GNSS data with road‐map database (i.e., Map‐matching) and to estimate the location via the velocity (Dead Reckoning) (Liu et al , ; Zhang and Gao, ). Map‐matching, however, requires directional constraints such as roads and specific lanes and Dead Reckoning requires appropriate sensors, such as odometry in cars and accelerometers used for step detection on pedestrians (Groves, ; Bissig et al , ; Mather et al , ). Recently, a new approach has been introduced to tackle this issue: Shadow Matching, originally introduced by (Groves, ).…”

Indoor Taxi-Cab: Real-Time Indoor Positioning and Location-Based Services with Ekahau and Android OS

“…Lots of studies have been done and remarks that specific calibration techniques are needed to be able to obtain accurate positioning and mapping results related with site characteristics. Previous work provide an overview of indoor positioning techniques [4][5] [6].…”

“…• Motion Estimation: In the literature, a number of object motion estimation methods are described and used [4] e.g. the radio-frequency (RF) based RADAR system: Radar is a sensor, which can be placed on walls in order to gather Wi-Fi signal strength information at multiple locations to triangulate the object's coordinates [9][10].…”

Foodflare: An Indoor Navigation System

“…A similar approach is implemented by [14], which also incorporates human motion analysis for reduced error. Pascal Bissig [5,12] gathers information from WLAN signal strength of nearby routers and generates a map by consolidating the information. Similarly, [13] gathers information from RSSI fingerprints and google maps data.…”