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Project 1: Integrity Monitoring for Network-RTK Systems

Impact of errors in tropospheric zenith wet delays on simulated rover positions.

Nowadays, relative positioning solutions from network RTK based on GNSS carrier phase observations are needed to enable few-cm accurate navigation. This is especially true for automated car navigation. However, integrity information associated to network RTK positioning is still missing. In the proposed project, a theoretical concepts for integrity monitoring in active reference stations should be developed, implemented in software and tested with real data from the mapathon test drives. This includes studying and modeling the data quality of GNSS data of the reference stations, the status of ambiguity fixing, the transfer of remaining systematics to the user via error correction models for tropospheric or ionospheric refraction. The correction streams are either represented as areal correction parameters or data for a virtual reference station. As a result, strategies for integrity monitoring, identification of most dominant error contributions as well as a validation with real data are expected.



Wübbena G., Schmitz M., Bagge A. (2005): PPP-RTK: Precise Point Positioning Using State-Space Representation in RTK Networks. Proc ION GNSS September 13 - 16, 2005: 2584 - 2594

Schön S., Brenner C., et al (2018): Integrity and Collaboration in Dynamic Sensor Networks. Sensors 2018,18(7). DOI: 10.3390/s18072400

Fang Y., Wang J. (2008): GPS RTK Performance Characteristics and Analysis. Journal of Global Positioning Systems 7(1) :1-8.

Project 2: Quality Estimation of Multi-Sensor-Systems (MSS) in a collaborative network

Results of intensity based approach (10 TLS standpoints), presentation with standard deviation for the 3D coordinates of each point [0, 3] mm. Out of range values marked in grey [Stenz et al. 2018]

Nowadays, the judgement of the quality of results (including integrity) for dynamic sensor networks is an important issue. This is of great importance when the results are used, e.g., in the context of (relative) positioning of autonomous cars. A typical example is the uncertainty of the mapped environment (point cloud) from a multi-sensor system (MSS) which is not homogeneous and depends on many different influencing factors. These include the sensor properties, referencing, scan geometry (e.g., distance and angle of incidence), environmental conditions (e.g., atmospheric conditions) and the scanned object properties (e.g., material, color and reflectance). Depending on the mentioned influence factors, quality parameters, quality indices and classes of quality need to be estimated. Within this project an extended uncertainty budget of an MSS is developed and transferred to a typical mapping application in a collaborative MSS network. The main aim then lies, on the one hand, on the determination of the uncertainty budget and other quality parameters, on the other hand on the judgement of its improvement due to the used collaborative dynamic MSS network. This allows for the first time a quantification of the benefit of using collaboration networks by judging the improvement of the uncertainty, reliability and integrity. Filter solutions will be already available for the dynamic MSS as a starting point for the project.


Stenz U., Hartmann J., Paffenholz J.-A., Neumann I. (2017): A Framework Based on Reference Data with Superordinate Accuracy for the Quality Analysis of Terrestrial Laser Scanning-Based Multi-Sensor-Systems, Sensors 2017, 17(8), 1886. DOI: 10.3390/s17081886.

Stenz U., Paffenholz J.-A., Genz T., Timmen A., Neumann, I. (2018): Quality assurance and visualization of the results from terrestrial laser scanning in near real time. Under review for Journal Photogrammetry and Remote Sensing.

Sun L., Dbouk H., Neumann I., Schön S., Kreinovich, V.  (2017): Taking into Account Interval (and Fuzzy) Uncertainty Can Lead to More Adequate Statistical Estimates, In: Melin, P.; Castillo, O.; Kacprzyk, J.; Reformat, M.; Melek W. (Hrsg.) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, Band 648, S. 371-381. Springer, Cham. DOI: 10.1007/978-3-319-67137-6_41.

Project 3: Collaborative positioning using imaging sensors

In this dissertation project we want investigate how the original sensor data (e.g. image gray values), derived variables (e.g. image features) and relative poses can best be used for highly reliable collaborative positioning with GNSS and image sensors in dynamic sensor networks, in which the nodes recognize each other and exchange information about their states. can best be combined. The basis of the project are approaches of photogrammetric point determination by means of bundle adjustment, which are to be extended in suitable ways. Here, the architecture of the overall solution (central vs. decentralized) and the basic determinability of a solution including its quality must be taken into account, also under limited conditions. Using a digital map, simulated and real data for different scenarios, it is to be investigated to what extent the collaboration enables sufficiently accurate and reliable positioning even in areas where sufficient GNSS information is not available, such as in densely populated roads with GNSS shadowing.


Grafarend E. W. & Sansò F. (Hg.) (1985): Optimization and Design of Geodetic Networks. Berlin: Springer-Verlag.

Lee J. K., Grejner-Brzezinska D. A. & Toth C. K. (2012): Network-based collaborative navigation in GPS-denied environment. Journal of Navigation 65 (03), S. 445–457. DOI: 10.1017/S0373463312000069.

Molina P., Blázquez M., Cucci D.A., Colomina I. (2017): First Results of a Tandem Terrestrial-Unmanned Aerial mapKITE System with Kinematic Ground Control Points for Corridor Mapping. Remote Sens. 2017, 9, 60. DOI: 10.3390/rs9010060

Unger J.; Rottensteiner F.; Heipke C., 2017: Assigning Tie Points to a Generalised Building Model for UAS Image Orientation. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6, pp. 385-39. DOI: 10.5194/isprs-archives-XLII-2-W6-385-2017.



Project 4: Combining probabilistic and interval-based error models for sensor fusion on mobile robots

Interval-based methods are used in the field of mobile robotics to put reliable bounds on the results of different approaches (e.g. localization, map building, obstacle detection). These methods return an interval (lower and upper bound) containing the true solution. However, often such an interval is not sufficiently precise as it does not provide a working point as input for a controller.

Nevertheless, methods relying on interval analysis have many advantages over probabilistic approaches (e.g. guaranteed results under given assumptions, ability to model systematic errors, no linearization errors). They can be used to eliminate definitely wrong results while making no explicit statement about the true solution, which is only guaranteed to reside in the resulting interval. Naively, the midpoint of an interval can serve as a point-valued solution.

On the other hand, probabilistic approaches are widely used for different problems in the field of mobile robotics. By design, they return point-valued results, which, however, cannot be guaranteed and may be significantly wrong due to systematic or linearization errors. The aim of this project is to combine probabilistic and interval-based methods for robot control to guarantee the integrity of the solution. On the other hand, this enables us to provide point-valued results for further computations.


Jaulin L., Kieffer M., Didrit O. and Walter É. (2001): Applied Interval Analysis. Springer London.

Neuland R., Maffei R., Jaulin L., Prestes E. and Kolberg M. (2014): Improving the precision of AUVs localization in a hybrid interval-probabilistic approach using a set-inversion strategy. Unmanned Systems, Vol. 02, no. 04, pp. 361–375.

Langerwisch M. and Wagner B. (2012): Guaranteed mobile robot tracking using robust interval constraint propagation. Lecture Notes in Computer Science, pp. 354–365, 2012. DOI: 10.1007/978-3-642-33515-0_36.

Project 5: Integrity measures for hierarchical and incremental map data acquisition

Data acquired collaboratively by several traffic participants is characterized by different levels of detail, accuracy and completeness. Integrating and assembling this information to a consistent dynamic map requires integrating and propagating quality and integrity measures across the different levels of representation. The project involves the definition of multiscale representations of quality measures, as well as mechanisms for the propagation of those measures across the different representations. Methods from cartographic generalization, machine learning and optimization will be required.







Sester, M., Arsanjani, JJ., Klammer, R., Burghardt, D. & Haunert, JH. (2014): Integrating and Generalising Volunteered Geographic Information. Abstracting Geographic Information in a Data Rich World, Springer, 119‐155.

Schulze, M.; Brenner, C. & Sester, M. (2012): Cooperative information augmentation in a geosensor network, Advances in Geo‐Spatial Information Science, CRC Press

Hampe, M.; Sester, M. & Harrie, L. (2004): Multiple representation databases to support visualisation on mobile devices. In: Altan, O. (Hg.): XXth ISPRS Congress, Technical Commission IV, B4. XXth ISPRS Congress. Istanbul (ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXV), S. 135–140.

Sester, M. (2005): Optimizing Approaches for Generalization and Data Abstraction, International Journal of Geographic Information Science, vol. 19 Nr. 8-9, pp. 871-897