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Sensor Fusion for Rockfall Sensor Node

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StoneNodeFieldTests.jpg
Tramin.jpg
RAMMS Rockfall.png

Short Description

Over the last years, SLF Davos has been using embedded sensor nodes for measuring trajectories of falling rocks. The acquired data is used to improve simulations of rockfall incidents. However, the employed sensor nodes (StoneNode) have come to their end-of-life and are currently being replaced with better and smaller sensors employing state-of-the-art technology. Over the last months, a new StoneNode has been developed at the IIS, and preliminary field test data has recently been obtained.

In this master project you are going to analyze the obtained data from accelerometers/gyroscopes and pressure sensors, and develop sensor fusion approaches which will allow partial or complete reconstruction of the rock trajectories. Depending on the project progress, novel approaches introducing additional sources of information can be evaluated as well (e.g., videos shot by a drone, 3D terrain models, etc.).


Status: Available

  • Looking for 1 Master Student
Supervisors: Michael Schaffner, Lukas Cavigelli, Michele Magno, Andrin Caviezel (SLF Davos)

Prerequisites

  • Good signal processing background required
  • MATLAB, C++ required
  • Computer vision course (optional)

Character

30% Theory
30% Implementation
40% Evaluation

Partners

SLF Davos

Professor

Luca Benini

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