Simon Schmitt

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.

A survey of experimental evaluation in indoor localization research

During the last decade, research in indoor localization and navigation has focused on techniques, protocols, and algorithms. The first International Conference on Indoor Positioning and Indoor Navigation (IPIN) was held in 2010. Since then, this annual conference showed the progress of research and technology. The variations of evaluation methods are significant in this field: they range from none, to extensive simulations, and real-world experiments under non-lab conditions. We look at the articles published in the proceedings of IPIN by IEEE Xplore from 2010 to 2014, and analyze the development of evaluation methods. We categorized 183 randomly selected papers, in respect to five different aspects. Namely: (1) the underlying system/technology in use, (2) the evaluation method for the proposed technique, (3) the method of ground truth data gathering, (4) the applied metrics, and (5) whether the authors establish a baseline for their work.

Experimental evaluation of RF-based indoor localization algorithms under RF interference

In the current practice, the performance evaluation of RF-based indoor localization solutions is typically realized in non-standardized environments and following ad-hoc procedures, which hampers objective comparison and does not provide clear insight into their intrinsic properties. Many evaluation procedures also neglect important environmental factors like RF interference, diminishing the real-world value of the obtained results. Localization competitions, in which different solutions are evaluated along a set of standardized metrics under unified and representative conditions, can play an important role in mitigating these problems, but their organization is cost and labor intensive. In this paper we report on the design, execution and results from an online localization competition in which several different RF-based indoor localization algorithms have been evaluated with the help of a remotely accessible and automated testbed infrastructure, that reduces these overheads. The competing algorithms have been evaluated following a combination of precision, latency and sensitivity metrics, under four different benchmarking scenarios, resulting in 28 different benchmarking experiments. The obtained results provide strong indication that specific types of RF-interference noticeably degrade the localization performance.

The effects of human body shadowing in RF-based indoor localization

In radio frequency based indoor human localization systems with body mounted sensors, the human body can cause non-line-of-sight (NLOS) effects which might result in severe range estimation and localization errors. However, previous studies on the impact of the human body only conducted static experiments in controlled environments. We confirm known effects and conduct real-world experiments in a typical indoor human tracking scenario using 2.4 GHz time of flight (TOF) range measurements. We analyze the effect on the raw measurements and on the localization results using the localization algorithms Centroid, NLLS, MD-Min-Max, and Geo-n. The experiment design is focused on incident management, where an infrastructure might only be installed in front of the building. We show that these effects have considerable impact on the localization accuracy of the person.

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.

Device-free indoor localisation using radio tomography imaging in 800/900 MHz band

Radio tomographic imaging (RTI) can be used as a method for device free localisation of persons in rooms. By measuring the signal strength of all links of a network of sensor nodes, one can estimate the position of an attenuating object with reasonable precision. The sub-GHz is shown to be suitable for an implementation. Such a design is more energy efficient than a 2.4 GHz implementation. We adapt, evaluate, and improve the device-free localization method of Wilson and Patwari to indoor environments using the 800/900 MHz band. The advantage of using 800/900 MHz is reduced reflections compared to 2.4 GHz. At the same time, the signal is attenuated less by objects in its path. Thus, the methods of Wilson and Patwari needed to be refined and parameters needed to be adapted. We evaluate some combinations of the most common choices of norms to perform a Tikhonov regularisation. The first difference gradient operator with H1 norm works best. We equipped a 5 m×5 m room with 20 wireless sensor nodes. We evaluated the influence of the distance to walls and the height of nodes. The radio tomographic image are post-processed by filters to make likely positions of objects more apparent. Additional parameters values suggested by Wilson and Patwari could not be used for the new frequency and hardware. For example, the ellipse excess path length has been experimentally determined to be close to 20 cm instead of 2 cm. In our experiments-up, we achieve an a maximum average localisation error below of 78 cm. With this work, we have reproduced the results of Wilson and Patwari, adapted it to a different frequency in the 800/900 MHz band and developed improvements to the original algorithms.

Path loss and multipath effects in a real world indoor localization scenario

This paper presents results of a large real world experimental setup of an indoor localization system. We used a time-of-flight based radio range measurement system to collect a large body of ranging data between a mobile reference system and multiple anchor nodes with a fixed and known position. For our experiment the reference system moved autonomously through an office building while collecting ranging data. We used this data to analyze the impact of environmental parameters on the ranging accuracy. We saw effects which are not predicted by the standard channel models for multiple scenarios and discuss these effects in detail.

Experimental evaluation of indoor localization algorithms

In Radio Frequency (RF)-based indoor localization scenarios, localization algorithms are needed to alleviate the impact of non-line-of-sight and multipath effects on the measurements and thereby estimate the true position precisely. Several resilient lateration algorithms have been proposed in the last couple of years which claim to minimize these effects. However, most of these algorithms were only evaluated using simulations or small static testbeds. We conducted an experiment using 25 anchor nodes and a mobile node installed on top a robotic reference system to collect ranging values. The robot has a localization error of 6.5cm which is an order lower than our range measurement errors. We use this robot to collect range measurements and ground truth positions along a densely grid with approx. 10 cm spacing. The experiment was carried out in a hallway of our office-like building. We collected data on approx. 300 m2. First, we examine the influence of the anchor placement and anchor density on the ranging errors we see. Then, we evaluate and analyze the robustness of localization algorithms on our measured data to decide which one works best for a constellation of anchor placement and building. Our results show, that there are significant differences between the simulations published for lateration algorithms and actual experiments in real-world indoor localization scenarios. As we show in this paper, the distance measurement error distribution has a large influence on these algorithms.

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.

Conditional erosion to estimate routing graph out of floor plans

Systems for indoor navigation differ substantially in implementation and maintenance effort as well as in costs. A system must work on any smart phone to ensure broad adoption and avoid isolated solutions. It must also work as automated as possible. A routing graph is commonly used for path planning. But generally, no routing graph exists and it must be computed. We propose a method to compute a routing graph from floor plans. We use conditional erosion to extract the graph. An approximation to the common routes through corridors and rooms can be calculated by a conditional query of every pixel of the grid based floor plan by means of predefined 3 × 3 image matrices. The grid data is then converted to edges and nodes. We evaluate the method on existing floor plan data of a test building of the HafenCity University of Hamburg.