Michael Angermann

Distributed robotic sensor networks: An information-theoretic approach

In this paper we present an information-theoretic approach to distributively control multiple robots equipped with sensors to infer the state of an environment. The robots iteratively estimate the environment state using a sequential Bayesian filter, while continuously moving along the gradient of mutual information to maximize the informativeness of the observations provided by their sensors. The gradient-based controller is proven to be convergent between observations and, in its most general form, locally optimal. However, the computational complexity of the general form is shown to be intractable, and thus non-parametric methods are incorporated to allow the controller to scale with respect to the number of robots. For decentralized operation, both the sequential Bayesian filter and the gradient-based controller use a novel consensus-based algorithm to approximate the robots’ joint measurement probabilities, even when the network diameter, the maximum in/out degree, and the number of robots are unknown. The approach is validated in two separate hardware experiments each using five quadrotor flying robots, and scalability is emphasized in simulations using 100 robots.

FootSLAM: Pedestrian Simultaneous Localization and Mapping Without Exteroceptive Sensors—Hitchhiking on Human Perception and Cognition

In this paper, we describe FootSLAM, a Bayesian estimation approach that achieves simultaneous localization and mapping for pedestrians. FootSLAM uses odometry obtained with foot-mounted inertial sensors. Whereas existing approaches to infrastructure-less pedestrian position determination are either subject to unbounded growth of positioning error, or require either a priori map information, or exteroceptive sensors, such as cameras or light detection and ranging (LIDARs), FootSLAM achieves long-term error stability solely based on inertial sensor measurements. An analysis of the problem based on a dynamic Bayesian network (DBN) model reveals that this surprising result becomes possible by effectively hitchhiking on human perception and cognition. Two extensions to FootSLAM, namely, PlaceSLAM, for incorporating additional measurements or user provided hints, and FeetSLAM, for automated collaborative mapping, are discussed. Experimental data that validate FootSLAM and its extensions are presented. It is foreseeable that the sensors and processing power of future devices such as smartphones are likely to suffice to position the bearer with the same accuracy that FootSLAM achieves with foot-mounted sensors already today.

Characterization of the indoor magnetic field for applications in Localization and Mapping

To improve our understanding of the indoor properties of the perturbed Earths magnetic field, we have developed a methodology to obtain dense and spatially referenced samples of the magnetic vector field on the grounds surface and in the free space above. This methodology draws on the use of various tracking techniques (photometric, odometric, and motion capture) to accurately determine the pose of the magnetic sensor, which can be positioned manually by humans or autonomously by robots to acquire densely gridded sample datasets. We show that the indoor magnetic field exhibits a fine-grained and persistent micro-structure of perturbations in terms of its direction and intensity. Instead of being a hindrance to indoor navigation, we believe that the variations of the three vector components are sufficiently expressive to form re-recognizable features based on which accurate localization is possible. We provide experimental results using our methodology to map the magnetic field on the grounds surface in our indoor research facilities. With the use of a magnetometer and very little computation, these resulting maps can serve to compensate the perturbations and subsequently determine pose of a human or robot in dead reckoning applications.

Magnetic maps of indoor environments for precise localization of legged and non-legged locomotion

The magnetic field in indoor environments is rich in features and exceptionally easy to sense. In conjunction with a suitable form of odometry, such as signals produced from inertial sensors or wheel encoders, a map of this field can be used to precisely localize a human or robot in an indoor environment. We show how the use of this field yields significant improvements in terms of localization accuracy for both legged and non-legged locomotion. We suggest various likelihood functions for sequential Monte Carlo localization and evaluate their performance based on magnetic maps of different resolutions. Specifically, we investigate the influence that measurement representation (e.g., intensity-based, vector-based) and map resolution have on localization accuracy, robustness, and complexity. Compared to other localization approaches (e.g., camera-based, LIDAR-based), there exist far fever privacy concerns when sensing the indoor environments magnetic field. Furthermore, the required sensors are less costly, compact, and have a lower raw data rate and power consumption. The combination of technical and privacy-related advantages makes the use of the magnetic field a very viable solution to indoor navigation for both humans and robots.

Continuous location and direction estimation with multiple sensors using particle filtering

In this paper we discuss the use of particle filtering to estimate the values of several state variables describing a users context. Since particle filtering algorithms are computationally efficient realizations of Bayesian filters they perform exceptionally well to optimally combine the a priori knowledge stemming from behavioral models, such as movement models, and the noisy measurements from sensors. The estimate at each time step is obtained in the form of a probability density function that represents the entire information and quantifies the inherent uncertainty about the context. The concept has been realized in simulations and experiments. In this paper, the applied movement model is presented with simulated measurements from GPS and compass sensors to illustrate the concept

Simultaneous Localization and Mapping for pedestrians using distortions of the local magnetic field intensity in large indoor environments

We present a Simultaneous Localization and Mapping (SLAM) algorithm based on measurements of the ambient magnetic field strength (MagSLAM) that allows quasi-real-time mapping and localization in buildings, where pedestrians with foot-mounted sensors are the subjects to be localized. We assume two components to be present: firstly a source of odometry (human step measurements), and secondly a sensor of the local magnetic field intensity. Our implementation follows the FastSLAM factorization using a particle filter. We augment the hexagonal transition map used in the pre-existing FootSLAM algorithm with local maps of the magnetic field strength, binned in a hierarchical hexagonal structure. We performed extensive experiments in a number of different buildings and present the results for five data sets for which we have ground truth location information. We consider the results obtained using MagSLAM to be strong evidence that scalable and accurate localization is possible without an a priori map.

A high precision reference data set for pedestrian navigation using foot-mounted inertial sensors

In this paper we present a reference data set that we are making publicly available to the indoor navigation community [8]. This reference data is intended for the analysis and verification of algorithms based on foot mounted inertial sensors. Furthermore, we describe our data collection methodology that is applicable to the analysis of a broad range of indoor navigation approaches. We employ a high precision optical reference system that is traditionally being used in the film industry for human motion capturing and in applications such as analysis of human motion in sports and medical rehabilitation. The data set provides measurements from a six degrees of freedom foot mounted inertial MEMS sensor array, as well as synchronous high resolution data from the optical tracking system providing ground truth for location and orientation. We show the use of this reference data set by comparing the performance of algorithms for an essential part of pedestrian dead reckoning systems for positioning, namely identification of the rest phase during the human gait cycle.

Context modelling and management in ambient-aware pervasive environments

Services in pervasive computing systems must evolve so that they become minimally intrusive and exhibit inherent proactiveness and dynamic adaptability to the current conditions, user preferences and environment. Con-text awareness has the potential to greatly reduce the human attention and interaction bottlenecks, to give the user the impression that services fade into the background, and to support intelligent personalization and adaptability features. To establish this functionality, an infrastructure is required to collect, manage, maintain, synchronize, infer and disseminate context information towards applications and users. This paper presents a context model and ambient context management system that have been integrated into a pervasive service platform. This research is being carried out in the DAIDALOS IST Integrated Project for pervasive environments. The final goal is to integrate the platform developed with a heterogeneous all-IP network, in order to provide intelligent pervasive services to mobile and non-mobile users based on a robust context-aware environment.

Improving Simultaneous Localization and Mapping for pedestrian navigation and automatic mapping of buildings by using online human-based feature labeling

In this paper we present an extension to odometry based SLAM for pedestrians that incorporates human-reported measurements of recognizable features, or “places” in an environment. The method which we have called “PlaceSLAM” builds on the Simultaneous Localization and Mapping (SLAM) principle in that a spatial representation of such places can be built up during the localization process. We see an important application to be in mapping of new areas by volunteering pedestrians themselves, in particular to improve the accuracy of “FootSLAM” which is based on human step estimation (odometry). We present a description of various flavors of PlaceSLAM and derive a Bayesian formulation and particle filtering implementation for the most general variant. In particular we distinguish between two important cases which depend on whether the pedestrian is required to report a places identifier or not. Our results based on experimental data show that our approach can significantly improve the accuracy and stability of FootSLAM and this with very little additional complexity. After mapping has been performed, users of such improved FootSLAM maps need not report places themselves.

A new mobility model based on maps

In this paper we present a mobility model for simulations of wireless access scenarios. This new model has been developed to reduce the shortcomings of previous models in capturing higher-order statistical dependencies in user behavior. The proposed model strikes a balance between the abstraction level of some purely stochastic path generation models that lack certain important properties of the real world and completely deterministic models that fail to produce answers to inherently stochastic problems such as call admission control, hand-off/handover or prefetching of data. The proposed model uses freely definable building maps and radio cell coverage information and generates sensible paths between randomly selected way-points. The paths are generated by a combined diffusion/steepest gradient algorithm. Various models for user speeds can be incorporated. The resulting patterns of user mobility and temporal radio coverage are well applicable in driving simulations with the purpose of addressing key questions in mobile wireless scenarios.