Martin Frassl

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.

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 prototyping environment for interaction between a human and a robotic multi-agent system

In this paper we describe our prototyping environment to study concepts for empowering a single user to control robotic multi-agent systems. We investigate and validate these concepts by experiments with a fleet of hovering robots. Specifically, we report on a first experiment in which one robot is equipped with an RGB-D sensor through which the user is enabled to directly interact with a multi-agent system without the need to carry any device.

Collaborative Mapping for Pedestrian Navigation in Security Applications

In rescue missions or law enforcement applications, accurate determination of every team member’s position and providing this information on a map may significantly improve mutual situation awareness and potentially reduce the risk of accidentally harming a team member. Furthermore, it could help keep track of the areas that have been already visited, helping the coordination of the mission at hand.

Mission review of aerial robotic assessment — Ammunition explosion cyprus 2011

After a fatal explosion on a Cypriot naval base, aerial robots were requested and deployed to assist in the assessment of the situation. The explosion had caused fatalities and severely damaged a nearby power plant which had contributed nearly half of the electrical power of the island. Enduring danger of collapse and falling debris interdicted access by humans to most parts of this critical infrastructure. Hence, aerial robots were used to conduct low altitude aerial assessment of roofs, load-bearing primary structures and other inaccessible parts. Assessment flights were carried out in a series of rapid iterations with disaster experts, structural and electrical engineers who specified flight objectives. This paper gives an account of this aerial robotic assessment mission with a focus on operational aspects and lessons learned.

Characterization of planar-intensity based heading likelihood functions in magnetically disturbed indoor environments

Heading information is a critical input to pedestrian dead reckoning. Unlike in most outdoor environments, the magnetic field inside of buildings is often strongly perturbed and inhomogeneous. Hence, straightforward approaches to use measurements of two-axis and three-axis magnetometers perform poorly. In recent measurements we have observed statistical properties of the magnetic field indicating that knowledge of the measured horizontal magnetic intensity is informative about the expected deviation of the measured magnetic heading. This statistical dependence has been quantified based on indoor measurements that have been collected in several offices and corridors in 3 buildings having different building orientations. A decrease in the spread of the horizonal angle is exhibited for larger horizontal intensities suggesting that measurements with large horizontal intensities are more reliable. We provide an approach to determine a likelihood function for the measured magnetic heading as a function of the local magnetic intensity in indoor environments. We show how the Expectation Maximization (EM) algorithm is used to construct a parametric two dimensional distribution of heading and planar-intensity, which can serve as a heading likelihood function in Bayesian positioning estimators, based upon which, greater weight is given to the less disturbed (strong-intensity) heading measurements and lower weight to the more erroneous ones (low-intensity). Drawing on our empirical data we show the performance improvement achieved with this new likelihood function.

Micro Aerial Vehicles in Disaster Assessment Operations – The Example of Cyprus 2011

This paper describes the deployment of Micro Aerial Vehicles (MAVs) during a European Civil Protection Mechanism mission to Cyprus in July 2011. Extensive damages at an industrial structure were assessed and evaluated. The utilized aerial robotic platforms, operations and findings are described. This deployment is an example of an situation-adaptive usage of MAVs in response to a man-made disaster.