Multipath Mitigation for GNSS-based Vehicle Localization
A common challenge for vehicle localization based on global navigation satellite systems (GNSS) is the multipath problem when high buildings block the direct line of sight to one or several satellites.
The blocked signals may still reach the receiver on the ground via one or several reflections on building structures or the ground. Since the signal path is longer for the reflected signal, ranging errors occur that can either prolongate the observed pseudorange or, due to correlation effects, shorten it. This leads to severely biased position estimates.
We work on methods for mitigation
of such effects and apply approaches of robust estimation using graphical models.
Robust Optimization for Pose Graph SLAM
Current state of the art solutions of the SLAM problem are based on efﬁcient sparse optimization techniques and represent the problem as probabilistic constraint graphs.
For example in pose graphs the nodes represent poses and the edges between them express spatial information (e.g. obtained from odometry) and information on loop closures. The task of constructing the graph is delegated to a front-end that has access to the available sensor information.
The optimizer, the so called back-end of the system, relies heavily on the topological correctness of the graph structure and is not robust against misplaced constraint edges. Especially edges representing false positive loop closures will lead to the divergence of current solvers.
We work on a novel problem formulation
that allows the back-end to change parts of the topological structure of the graph during the optimization process. The back-end can thereby discard loop closures and converge towards correct solutions even in the presence of false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers.
SkeyeCopter - Micro Aerial Vehicles for Civil Applications
Our research interests focus on enabling autonomous micro aerial vehicles (MAVs) to be applicable in a variety of civil applications, mainly in the areas of emergency response, disaster control, and environmental monitoring. These scenarios require a high level of autonomy, reliability and general robustness. In the SkeyeCopter-project we concentrate on intelligent algorithms for environmental perception, sensor fusion and control strategies. Three Hummingbird micro UAVs are used for validation and experiments.
Place Recognition by Simple Means: BRIEF-Gist
The ability to recognize known places is an
essential competence of any intelligent system that operates
autonomously over longer periods of time. Approaches that
rely on the visual appearance of distinct scenes have recently
been developed and applied to large scale SLAM scenarios.
FAB-Map is maybe the most successful of these systems.
We propose BRIEF-Gist
, a very simplistic
appearance-based place recognition system based on the BRIEF
descriptor. BRIEF-Gist is much more easy to implement and
more efficient compared to recent approaches like FAB-Map.
Despite its simplicity, we can show that it performs comparably
well as a front-end for large scale SLAM. We benchmark our
approach using two standard datasets and perform SLAM on
the 66 km long urban St. Lucia dataset.
Application of Biologically Inspired Methods for Autonomous Navigation and SLAM
Since the 1970's different types of neurons involved in spacial navigation have been identified in the brains of rats, primates and humans. Several experiments of different researchers show that these cells code the position and orientation of the animal in its environment.
Neurologists and theoretical biologists have been developing models that help to understand the behaviour of animals as well as humans in different situations. These models explain the workings of the different cell assemblies and their connections with other areas in the brain.
We are interested in these developements and want to derive efficient algorithms that help solving navigation tasks and SLAM on autonomous mobile robots. Our goal is explicitly not to mimic the behaviour of the involved biological processes on the level of single neurons or even spike trains. Instead, we want to understand the principles behind these biological processes and project them onto higher levels of abstraction that are suitable for implementation and application on autonomous systems.
Simulation of Robots and Sensors
For our micro-UAV project SkeyeCopter
we felt the need to develop an interface to a simulation environment that would allow faster development and evaluation of algorithms on the behavioural level of a single UAV or swarms of them. We chose to use and build upon USARSim, a simulation framework widely used in the RoboCup community.
We developed interfaces between USARSim and the already existing software modules responsible for UAV control. This interface is completely transparent to higher software layers, allowing the same code to be run in the simulation as well as on the real hardware without any change.
Recently, we contributed some of our work to the USARSim community. Check out
e.g. our image server or the corrected sonar sensor.