You are here: Home Chairs Chair for Monitoring … Projects

Projects

Amy – Autonomous mobile robotic monitoring system for large-scale structures

The goal of Amy is the development of an autonomous mobile mapping system, which will be used especially for the inspection and monitoring of infrastructure elements. Amy consists of a mobile platform (robot vehicle), various sensors (including cameras, laser scanners) and data analysis software. The system will be modular - freely adaptable and equipped with open interfaces. In the medium term, Amy will be interconnected with other systems – the cooperation with other robots (including drones) will be in the focus of development.

Project manager:       Prof. Dr. Alexander Reiterer

Email:                        alexander.reiterer@ipm.fraunhofer.de

Funded by:                intern

 

SPENSER - Understanding and Predicting the Spatial and Temporal Variability of Snow Processes Under Different Vegetation Covers Combining Laser Observations and Point Measurements

A comprehensive knowledge about how various vegetation and forest structures change snow accumulation and ablation processes is especially important since forest covers are one of the most rapidly changing land cover types. Especially human activities such as tree harvesting, regardless if done as clear cutting or selective cutting, and subsequent reforestation change the forest structure suddenly and substantially. Furthermore, changing climatic conditions will also result in expanding forest and shrub areas above timber line and hence changing the properties of forest canopies. Improved simulation models of how these changes affect snow and all the associated, above mentioned, processes will be a valuable tool to assess any impact these forest and climate changes may have on snow processes.

Firstly, an innovative UAV mounted laser scanning device specifically aimed at providing spatially distributed information for snow cover observations will be designed, implemented and evaluated. Secondly, several different forest and vegetation plots will be selected and instrumented with existing sensors. These plots will then be observed during frequent flights with the newly designed laser scanning system. Data analysis and the deduction of improved research models are further indispensable steps to bring the project a successfully end.

Project manager:       Prof. Dr. Alexander Reiterer

Email:                        alexander.reiterer@ipm.fraunhofer.de

Funded by:                DFG

Link:                          Link

 

STREEM - Full Scale Testing of Tree Streamlining in Wind

Due to increasing global urbanisation, trees are becoming more and more important, especially for urban planning. However, trees, for all their positive attributes, pose a risk during storms. To minimise this risk, the behaviour of trees in tree-wind interactions must be known. This is currently being investigated with tree pull tests or in the wind tunnel.

Within the project STREEM, a multi-sensor system consisting of four cameras and a tree-motion system (MPU) is being developed to determine the deformation of trees in the wind. The camera system must provide 3D information about selected areas of the tree and their movement und it must be able to deal with difficult conditions such as solar radiation. The central challenge in the development will be the identification and linking of corresponding points in the image time series and the fusion of the camera data with the airflow and MPU data. In addition to synchronising the sensors and storing the data, another challenge is the local referencing of the cameras.

Project manager:       Prof. Dr. Alexander Reiterer

Email:                        alexander.reiterer@ipm.fraunhofer.de

Funded by:                DFG

Link:                          Link

 

Partially automated creation of object-based inventory models using multi-data fusion of multimodal data streams and existing inventory data – mdfBIM+

To make the transport infrastructure in Germany safe and effective, the more than 65,000 bridges must be regularly inspected, rehabilitated or even newly built if necessary. Both new construction and rehabilitation require accurate and meaningful as-built data. Existing plans are often incomplete or out of date. In the rarest cases, 3D models of the existing building are available. This is exactly where mdfBIM+ comes in. The aim of the project is to develop a semi-automated process and digital tools for data interaction and modification for the creation of georeferenced, object-based as-built models. With multimodal data acquisition of the as-is state by static and mobile laser scanning (LiDAR), drone-based as well as mobile terrestrial photogrammetry, a refined as-built model is to be created in combination with existing as-built data, e.g. analogue floor plans, CAD plans, building books, expert reports, SAP. Through extensive automation, this can be used efficiently for the existing infrastructure and in particular the large number of bridge structures.

The project part of the University of Freiburg (Chair for Monitoring of Large-Scale Structures) focuses on the analysis of the multimodally acquired data. 3D point clouds from the different sources are fused and semantically segmented before they are merged with the as-built plans.

Project manager:       Prof. Dr. Alexander Reiterer

Email:                        alexander.reiterer@ipm.fraunhofer.de

Funded by:                Federal Ministry of Transport and Digital Infrastructure

Link:                       

Finished Projects

 

ErFAsst - Advance of level of automation for evaluating the structural safety of bridges

Bridges and tunnels are critical elements of traffic infrastructures. The outage of these elements has huge consequences for life and limb as well as for the security of supply. The stress for bridges grows with the increase of goods traffic. Nowadays every three years an inspection of the bridges occurs with manual and expensive methods.

The inspection becomes semi-automated and cheaper with ErfASst. New sensors and algorithms shall accelerate the detection of cracks and the evaluation of its effect on stability and usability. The new methods are realised in a demonstrator.

Project manager:       Prof. Dr. Alexander Reiterer

Project duration         September 2019 - June 2021

Email:                        alexander.reiterer@ipm.fraunhofer.de

Funded by:                Sustainability Centre Freiburg

Link:                          Link (german)

 

RaVeNNA - 4pi - Digital platform with 4PI real-time endoimaging of endoscopic 3D-reconstruction, visualisation und post-rehabilitation support for patient with bladder cancer

Endoscopy gains in importance for surgery, because it could be performed minimal-invasive and gentle surgeries with endoscopy. 3D-models form endoscopic pictures enable a more precise and more efficient surgery preparation and post processing. The reconstruction of bladder from endoscopic picture is difficult due to different reasons.

RaVeNNA should solve the reconstruction problems with structure from motion. Inside the reconstructed 3D-model bladder cancer should be detected and monitored. The classification algorithm of the structures inside the bladder is based on deep learn, especially convultional neuronal networks (ConvNets) going to be used.

Project manager:       Prof. Dr. Alexander Reiterer

Email:                        alexander.reiterer@ipm.fraunhofer.de

Funded by:                Federal Ministry of Education and Research (BMBF)

Link:                          Link (german)