Kavitha Muthukrishnan

Towards smart surroundings: enabling techniques and technologies for localization

In this paper we identify the common techniques and technologies that are enabling location identification in a ubiquitous computing environment. We also address the important parameters for evaluating such systems. Through this survey, we explore the current trends in commercial products and research in the area of localization. Although localization is an old concept, further research is needed to make it really usable for ubiquitous computing. Therefore, we indicate future research directions and address localization in the framework of our Smart Surroundings project.

WLAN Location Sharing through a Privacy Observant Architecture

In the last few years, WLAN has seen immense growth and it will continue this trend due to the fact that it provides convenient connectivity as well as high speed links. Furthermore, the infrastructure already exists in most public places and is cheap to extend. These advantages, together with the fact that WLAN covers a large area and is not restricted to line of sight, have led to developing many WLAN localization techniques and applications based on them. In this paper we present a novel calibration-free localization technique using the existing WLAN infrastructure that enables conference participants to determine their location without the need of a centralized system. The evaluation results illustrate the superiority of our technique compared to existing methods. In addition, we present a privacy observant architecture to share location information. We handle both the location of people and the resources in the infrastructure as services, which can be easily discovered and used. An important design issue for us was to avoid tracking people and giving the users control over who they share their location information with and under which conditions

Wireless Campus LBS: Building Campus-Wide Location Based Services Based on WiFi Technology

This paper describes a project that has just started at the University of Twente (UT) in cooperation with the International Institute for Geo-Information Science and Earth Observation (ITC) to provide Location Based Services (LBS) for the UT campus. This LBS will run on the existing Wireless Campus system that provides the whole 140 hectare University grounds with WiFi based internet access. The project serves as a testbed for research activities as well as an infrastructure to develop practical use cases upon. The former includes research into wireless LAN positioning techniques, into context awareness of ubiquitous data management systems, and into data dissemination for LBS and mobile applications. A first use case will be to provide the participants of SVGopen2005, the 4th Annual Conference on Scalable Vector Graphics (August 15-18, 2005) with an LBS to help them navigate the conference locations and locate fellow attendants.

Camera pose estimation using particle filters

In this paper we propose a pose estimation algorithm based on Particle filtering which uses LED sightings gathered from wireless sensor nodes (WSN) to estimate the pose of the camera. The LEDs act as (visual) markers for our pose estimation algorithm. We also compare the performance of our pose estimation algorithm against two reference algorithms — (i) Extended Kalman filtering (EKF) and (ii) Discrete Linear Transform (DLT) based approaches. The performance of all the three algorithms are evaluated for different camera frame rates, varying level of measurement noise and for different marker distribution. Our results (small-scale experimental and room-level simulation studies) show that the particle filtering algorithm gives an accuracy of a few millimetres in position and a few degrees in orientation.

Towards a rapidly deployable positioning system for emergency responders

Providing ad-hoc solutions for positioning and tracking of emergency response teams is an important and safety-critical challenge. Although solutions based on inertial sensing systems are promising, they are subject to drift. We address the problem of positional drift by having the responders themselves deploy beacons, specifically ultrasound beacons, as they progress into an unknown environment. Our research focuses on a sensor network approach that does not require any pre-deployment of infrastructure. This paper targets two important aspects within the context of providing positioning service for emergency responders namely on how to locate the deployed static beacons, and on how to track the responders by using a combination of ultrasound and inertial measurements. The main contributions of this paper are: (i) characterisation of the errors encountered in an inertial-based pedestrian dead reckoning solution and ultrasound range measurements in a mobile setting, (ii) an algorithm for ultrasound beacon localisation (using multidimensional scaling), (iii) formulation of a Kalman filtering based algorithm for tracking the responder using a combination of ultrasound range and inertial measurements, and (iv) the presented algorithms are evaluated using data collected from real deployments and are compared against an ultra-wideband (UWB) precision location system. Our approach of preventing the drift in inertial estimates by combining with ultrasound measurements are promising and offers a viable solution to providing positioning and tracking support to emergency responders.

Pose estimation with radio-controlled visual markers

Camera pose estimation (i.e. determining the position and orientation of a camera) found its applications, traditionally, in the field of virtual/augmented reality, gaming and robotics. In this paper we propose an inside-out system that uses LED sightings gathered from wireless sensor nodes (WSN) to estimate the pose of the camera. The LEDs act as (visual) markers for our pose estimation algorithm, which is based on Extended Kaiman filtering (EKF). We compare the performance of our EKF algorithm against an algorithm based on Discrete Linear Transform (DLT). We also consider the effectiveness of the presented algorithm for different camera frame rates, varying noise levels and varying LED visibility conditions using a mix of simulated and experimental data. Our initial results are promising and show that the EKF algorithm gives an accuracy of a few millimetres in position and few degrees in orientation, even under sparse LED conditions, low frame rates and high noise levels.

SLAM for Pedestrians and Ultrasonic Landmarks in Emergency Response Scenarios

Providing ad hoc solutions for positioning and tracking of emergency response teams is an important and safety-critical challenge. Although solutions based on inertial sensing systems are promising, they are subject to drift. We address the problem of positional drift by having the responders themselves deploy sensor nodes capable of sensing range and angle-of-arrival, as they progress into an unknown environment. Our research focuses on a sensor network approach that does not rely on preexisting infrastructure. This chapter targets two important aspects of such a solution: how to locate the deployed static sensor nodes, and how to track the responders by using a combination of ultrasound and inertial measurements. The main contributions of this chapter are: (i) a characterization of the errors encountered in inertial-based pedestrian dead-reckoning as well as ultrasound range and bearing measurements in a mobile setting, (ii) the formulation of an extended Kalman filter for simultaneously locating sensor nodes and tracking a pedestrian using a combination of ultrasound range/bearing measurements and inertial measurements, and (iii) the validation of the presented algorithms using data collected from real deployments.