Master Thesis - Anomaly Detection of Laser Scanner Data

Laser scanners are sensors that provide range data from a laser rotating in a plane. They are used in a wide range of applications, including measurement, navigation and safety. This thesis is about using Deep Learning techniques to detect anomalies in the laser scanner data.

This could include:

• Unexpected movements of people and/or vehicles in an area.
• Detection of outliers in measurement applications.
• Detecting sensor errors.

The goal is to be able to train the system on normal data to be able
to detect anomalies. Some relevant datasets are already available,
but collection of new data may be required.

Prerequisites

• Machine learning knowledge
• Programming in Python

Contact

For more information, contact R&D Manager Andreas Wrangsjö, andreas.wrangsjo@sick.se

We look forward to your application  before  the 18th of October!