Online Model Estimation of Ultra-Wideband TDOA Measurements for Mobile Robot Localization

Ultra-wideband (UWB) localization is a recent technology that promises to outperform many indoor localization methods currently available. Yet, non-line-of-sight (NLOS) positioning scenarios can create large biases in the time-difference-of-arrival (TDOA) measurements, and must be addressed with accurate measurement models in order to avoid significant localization errors. In this work, we first develop an efficient, closed-form TDOA error model and analyze its estimation characteristics by calculating the Cramer-Rao lower bound (CRLB). We subsequently detail how an online Expectation Maximization (EM) algorithm is adopted to find an elegant formalism for the maximum likelihood estimate of the model parameters. We perform real experiments on a mobile robot equipped with an UWB emitter, and show that the online estimation algorithm leads to excellent localization performance due to its ability to adapt to the varying NLOS path conditions over time.

Automatic Calibration of Ultra Wide Band Tracking Systems Using A Mobile Robot: A Person Localization Case-study

Alessio Canepa, Alcherio Martinoli, Zeynab Talebpour

Ultra Wide Band (UWB) is an emerging technology in the field of indoor localization, mainly due to its high performances in indoor scenarios and relatively easy deployment. However, in complex indoor environments, its positioning accuracy may drastically decrease due to biases introduced when emitters and receivers operate in Non Line-of-Sight (NLOS) conditions. This undesired phenomenon can be attenuated by creating, a priori, a map of the measurement error in the environment, that can be exploited at a later stage by a localization algorithm. In this paper, the error map is the result of a calibration process, which consists of collecting several measurements of the localization system at different locations in the environment. This work proposes the leveraging of mobile robots in order to automatize the calibration process with the ultimate purpose of improving UWB-based people localization in a realistic indoor environment. The whole process exploits existing algorithms in the field of robot localization conveniently adapted in order to address our use case and technology. Experiments in real environments of incrementally increasing complexity show how the average localization accuracy can be improved up to 50% by adopting this method.

Ieee2017

On-line Independent Support Vector Machines for Cognitive Systems

Barbara Caputo, Jie Luo

Learning from experience and adapting to changing stimuli are fundamental capabilities for artificial cognitive systems. This calls for on-line learning methods able to achieve high accuracy while at the same time using limited computer power. Research on autonomous agents has been actively investigating these issues, mostly using probabilistic frameworks and within the context of navigation and learning by imitation. Still, recent results on robot localization have clearly pointed out the potential of discriminative classifiers for cognitive systems. In this paper we follow this approach and propose an on-line version of the Support Vector Machine (SVM) algorithm. Our method, that we call On-line Independent SVM, builds a solution on-line, achieving an excellent accuracy vs.~compactness trade-off. In particular the size of the obtained solution is always bounded, implying a bounded testing time. At the same time, the algorithm converges to the optimal solution at each incremental step, as opposed to similar approaches where optimality is achieved in the limit of infinite number of training data. These statements are supported by experiments on standard benchmark databases as well as on two real-world applications, namely

(a)

place recognition by a mobile robot in an indoor environment, and

(b)

human grasping posture classification.

IDIAP2007

Indoor Navigation Research with the Khepera III Mobile Robot: An Experimental Baseline with a Case-study on Ultra-wideband Positioning

Adrian Arfire, Alexander Bahr, John Farserotu, Alcherio Martinoli, Amanda Stella Markowska Prorok

Recent substantial progress in the domain of indoor positioning systems and a growing number of indoor location-based applications are creating the need for systematic, efficient, and precise experimental methods able to assess the localization and perhaps also navigation performance of a given device. With hundreds of Khepera III robots in academic use today, this platform has an important potential for single- and multi-robot localization and navigation research. In this work, we develop a necessary set of models for mobile robot navigation with the Khepera III platform, and quantify the robot’s localization performance based on extensive experimental studies. Finally, we validate our experimental approach to localization research by considering the evaluation of an ultra-wideband (UWB) positioning system. We successfully show how the robotic platform can provide precise performance analyses, ultimately proposing a powerful approach towards advancements in indoor positioning technology.

2010

On-line Independent Support Vector Machines for Cognitive Systems

Barbara Caputo, Jie Luo

(a)

place recognition by a mobile robot in an indoor environment, and

(b)

human grasping posture classification.

IDIAP2007