Thomas Hillebrandt

A reference system for indoor localization testbeds

We present a low-cost robot system capable of performing robust indoor localization while carrying components of another system which shall be evaluated. Using off-the-shelf components, the ground truth positioning data provided by the robot can be used to evaluate a variety of localization systems and algorithms. Not needing any pre-installed components in its environment, it is very easy to setup. The robot system relies on wheel-odometry data of a Roomba robot, and visual distance measurements of two Kinects. The Robot Operating System (ROS) is used for the localization process according to a precise pre-drawn floor plan that may be enhanced with Simultaneous Localization and Mapping (SLAM). The system is able to estimate its position with an average error of 6.7 cm. It records its own positioning data as well as the data from the system under evaluation and provides simple means for analysis. It is also able to re-drive a previous test run if reproducable conditions are needed.

Quantitative and Spatial Evaluation of Distance-Based Localization Algorithms

Indoor localization, especially in wireless networks (WN) has become an important research focus in computer science during the past ten years. Several approaches exist to estimate a node’s position relative to other devices. Most approaches are based on distance measurements and localization algorithms. In this chapter we provide an overview of common and new localization algorithms. A detailed investigation on the error distribution and the real world behaviour of these algorithms is presented. We also provide a discussion of the evaluation results that leads to open questions and future research approaches.

The Membership Degree Min-Max localization algorithm

We introduce the Membership Degree Min-Max (MD-Min-Max) localization algorithm as a precise and simple lateration algorithm for indoor localization. MD-Min-Max is based on the well known Min-Max algorithm that uses a bounding box to compute the position. We present an analysis of the Min-Max algorithm and show strengths and weaknesses in the spatial distribution of the position error. MD-Min-Max uses a Membership Function (MF) based on an estimated error distribution of the distance measurements to gain a higher precision than Min-Max. The algorithm has the same complexity as Min-Max and can be used for indoor localization even on small devices, e.g. in Wireless Sensor Networks (WSNs). To evaluate the performance of the algorithm we compare it with other Min-Max algorithms in simulations and in a large real world deployment of a WSN.

A virtual indoor localization testbed for Wireless Sensor Networks

We present a novel, easy to use virtual testbed enabling researchers to evaluate their localization algorithms based on distance measurements in indoor environments. We provide precise ground truth information collected by our previously presented reference system, based on a mobile robot, in combination with range measurements from a Wireless Sensor Network (WSN) in multiple buildings. The user can define a virtual experiment using our datasets over the web. This approach separates the process of designing a robust indoor localization algorithm from the need for testing it on actual hardware in different scenarios.

The Geo-n localization algorithm

We introduce Geo-n, a highly precise, distancebased, general-purpose localization algorithm for use in cluttered and indoor environments, where the estimated distances between an unlocalized node and anchor nodes may contain large errors due to NLOS signal propagation. Geo-n is very resilient to outliers and to a wide range of geometrical constellations of the nodes. Geo-n uses a two stage filtering technique to obtain the most representative intersection points between every pair of circles induced by anchor coordinates and distance measurements and uses these to estimate the position of unlocalized nodes. If no intersection exists between two circles, Geo-n approximates one to further improve localization accuracy. We compare Geo-n to LLS, NLLS, AML, Min-Max, and ICLA using simulations and real world experiments, and show analysis of the spatial distribution of position errors. Results demonstrate that Geo-n outperforms the other algorithms. Like NLLS, its spatial error distribution is very homogeneous. However, Geo-n is much more robust and achieves lower average position error while retaining reasonable computational complexity.

The FU Berlin parallel lateration-algorithm simulation and visualization engine

We introduce a simulation engine to visually evaluate and compare distance based lateration algorithms and deployments called the FU Berlin Parallel Lateration-Algorithm Simulation and Visualization Engine (LS2). Our engine simulates a scenario which consists of given anchor positions and evaluates all positions of a playing field in parallel, instead of only randomly selected positions. At the end of a simulation run, the user is able to judge the strengths and weaknesses of the algorithm in a picture that displays the spatial position error distribution, a representation of positions in a plane with their errors. Understanding spacial distribution of error is especially valuable, because we observed that it depends on the placement of anchor nodes. This enables the developer to optimize his algorithm or aid him in selecting an algorithm for his application. The simulators design separates the simulation engine, the lateration algorithms, and the error models. The simulator can be easily extended with additional lateration algorithms and error models. The engine is written in GNU C99 and uses the SSE or AVX vector extensions of modern microprocessors. Thus, it is able to scale fully to all cores. Beside extendability, the main focus is set on execution speed.

Virtual testbed for indoor localization

We present a novel, easy to use virtual testbed for the evaluation of localization algorithms. Our testbed enables researchers to easily run tests on a huge body of real world range-based indoor localization data. The data consists of a dense grid of reference points belonging to one or multiple maps. Each point consists of a ground truth value and an arbitrary number of ranging values. Each ranging value belongs to a certain anchor node on a fixed position. The reference data is gathered by a robot which carries (arbitrary) localization devices. The robot stores its location as a ground truth value and simultaneously uses the localization device to measure the distance to a set of anchors in range. The ground truth value is gathered by an optical reference system which is applied to the robot. It is possible to define paths through a map using a web interface. Our system uses our experimental gathered reference points to deliver a dataset of ranging values for the current path. Therefore the researcher can run a virtual experiment by himself and can adjust several parameters. Our system enables other researchers to run reproducible experiments on real word data. The expensive and complex deployment of a dedicated infrastructure and experimental setup can be avoided as well as the error-prone task of modelling a localization system and running a simulation. Our system will be open to the research community and will help to develop a better understanding of the field of range based indoor localization.

Measuring the distance between wireless sensor nodes with standard hardware

Several ways to estimate the position of a Wireless-Sensor-Network (WSN) node have been discussed in the past years. Unlike in outdoor solutions where the Global Positioning System (GPS) could be used in most applications, a general solution for indoor usage has not been found. The few existing indoor localization solutions on the market are highly specialized and rely on infrastructure or on very expensive special designed hardware. Both - infrastructure and expensive hardware - do not fit well into most scenarios where WSNs are commonly used because of the adhoc characteristics and the large amount of nodes of such installations. In this paper we present a solution to get a precise estimation of the distance between two nodes without the needs for special purpose chips or a redesign of already existent nodes. We use radio runtime measurement to calculate the distance between nodes and present algorithms to refine the measurements. A comparison with a professional solution which is available on the market is also presented.

Wireless sensor networks in emergency scenarios: the FeuerWhere deployment

In the project FeuerWhere we researched the use of Wireless Sensor Networks (WSNs) in rescue scenarios by combining the monitoring of the environment and vital signs, as well as estimating the location of the nodes in a WSN. The goal of this project was to monitor vital signs, envi- ronmental conditions and positions of firefighters in a large indoor emergency scenario using a meshed WSN. The WSN consists of one node for each firefighter which transports the data to the mesh network and which is also connected to a Body Area Network (BAN) [8]. The BAN itself consists of several nodes placed into protective suits. The main requirement to all parts of the system is robustness against all kinds of extreme environmental conditions, like extreme air temperatures up to 800°C, extreme humidity up to 100% including condensation and wet walls in unknown buildings. We report on an experimental evaluation of the deployment of a prototype that addresses the concern of monitoring firefighters in extreme environmental conditions. This will establish the general feasibility of WSN in extreme conditions and show that precise indoor localization using radio runtime measurements is not affected by these conditions.