Slawomir Sander (Grzonka)

Slawomir Grzonka

Mapping, State Estimation, and Navigation for Quadrotors and Human-Worn Sensor Systems
Technical support for first responders, who have to explore hazardous environments to locate and rescue victims and thereby avoiding dangerous areas, has recently gained a substantial interest in the research community.

One possibility to assist first responders is by using robots to explore the environment and deitect dangerous areas or locate victims. Flying robots are envisioned to play a key role in this context as their increased mobility allows them to fly over obstacles where wheeled robots get stuck. In order to be able to operate indoors, the flying robot should be able to keep a stationary pose. All these prerequisites are met by quadrotors. Unfortunately, teaching human personnel to remotely steer such a flying platform is time intensive and costly. Additionally, manual piloting bears the risk of damaging the robot due to the confined space indoors and due to environmental conditions that can have a severe impact on the quality of the radio link. The flying robot therefore needs to be able to operate autonomously over an extended period of time. In this case, only minimal input from a human (e.g., the next location the robot should fly to) is required. Another possibility to support first responders is by using sensor systems that are integrated into their garment. Such sensors can provide vital information about the current location of the wearer or an approximate map of the environment. In the context of first responders operating in an unknown building, this information could be used to re-route the wearer to the nearest exit in case of emergency, especially if environmental conditions like smoke and fire elicit confusion among first responders. Even more, this kind of information can be employed in a search and rescue mission by improving the delegation of different teams, i.e., by avoiding searching the same area multiple times.

However, systems like the ones described so far (i.e., sensor systems mounted on a quadrotor or integrated into the garment) need to be aware of their current state, including their own location. To estimate the location indoors, a map of the environment is needed in most cases. In general, this map is not known beforehand and the (robotic) system needs to build a map of the environment based on its sensor measurement during the mission. Due to the limited computational power available, efficient mapping techniques are mandatory.

In this thesis we develop novel technologies for efficient mapping that can be used with a variety of sensors. We furthermore develop a navigation system that enables a small-sized flying robot (quadrotor) to fly autonomously indoors. Finally, we develop an approach to map indoor environments based on human motion recorded with a data suit, i.e., an embedded sensor system consisting of several inertial measurement units worn by the human.

In the first part of this thesis, we present an innovative technique for estimating the trajectory of a robot, given its observations. We will demonstrate that compared to other state-of-the-art approaches our approach is up to several orders of magnitude faster without any loss in accuracy. This allows embedded systems to efficiently and accurately recover their trajectory and thus allows them to build accurate maps of the environment. This general framework is subsequently used in the second part of the thesis in the corresponding embedded sensor systems.

In the second part, we develop two navigation systems for different types of sensor setups. The first navigation system enables a small-sized quadrotor to fly autonomously indoors. This includes pose control, localization, map building, path-planning, and obstacle avoidance. We also present a novel technique to map obstacles underneath the robot. The second system employs information recorded with a data suit consisting of several inertial measurement units worn by a human. We develop a solution to recover the trajectory of the human and to build a geometrical as well as topological map of the environment. In all cases, we solely employ the motions and detected activities of the human. The wearer, therefore, does not need to carry any additional sensors like cameras or laser scanners that would also not work reliably in the case of environmental conditions like smoke and fire.

Above, we motivated our work in particular envisioned for first responders. However, it is important to note that the developed technologies can be applied to a variety of scenarios and are not restricted to the field of search and rescue.


author = {Grzonka, Slawomir},
title = {Mapping, State Estimation, and Navigation for Quadrotors and Human-Worn Sensor Systems},
school = {Albert-Ludwigs-University of Freiburg},
year = {2011}

Document (pdf file):

thesis (41MB)