Author: stachnis

2020-11: IROS2020 Best Paper Award on Agri-Robotics

Dario Gogoll, Phillip Lottes, Jan Weyler, Nik Petrinic, and Cyrill Stachniss have won the IROS2020 Best Paper Award on Agri-Robotics with their paper: Unsupervised Domain Adaption for Transferring Plant Classification Systems to New Field Environments, Crops, and Robotics. The paper proposes an effective approach to unsupervised domain adaption for plant segmentation systems in agriculture and thus to adapt existing systems to new environments, different value crops, and other farm robots. Check out the paper trailer video if you want to learn more about this contribution.

2020-06: Lorenzo Nardi Defended His PhD Thesis


Over the last decade, the demand for autonomous mobile robots has been growing continuously. Applications range from mobile manipulators operating on factory floors to autonomous cars driving in urban environments. A common requirement for all these tasks is the capability to autonomously navigate by making sequences of decisions in environments that are complex, dynamic, and uncertain. Robots are often deployed in environments populated by humans or other moving objects and require to perform safe and compliant behaviors for navigation. Furthermore, real-world scenarios are typically characterized by uncertainty in the robot’s perception, action execution, and belief about the world. Traditional approaches to robot navigation plan and follow the shortest path on static geometric representations of the environment. Such systems are often not adequate to capture the characteristics of real-world environments and may lead robots to perform behaviors that are sub-optimal in practice.
In this thesis, we address robot navigation in different real-world scenarios and investigate a set of approaches that go beyond planning the shortest paths. We present solutions for robot navigation that are able to take into account and reason about the situation in which the robot navigates, the dynamics populating the environment, and the uncertainty about the world by exploiting available background knowledge. For example, we use publicly available maps of urban environments to planning policies for performing robust navigation on road net- works under position uncertainty. Whereas, we exploit the paths experienced by the robot during navigation to generate safe and predictable behaviors that meet the user’s preferences. We also present solutions for navigating in partially unknown environments by actively gathering information and by exploiting this knowledge to automatically improve robot navigation over time. We use the onboard robot perception during navigation in outdoor environments to automatically discover paths along which the impact of detrimental factors due to the terrain is lower. Furthermore, we exploit the observations about the traversability changes in an environment to plan anticipatory behaviors that lead the robot to encounter a reduced number of unforeseen obstacles while navigating.

2020-03-19: Remote Teaching during the COVID-19 Pandemic

Dear Students,

The current COVID-19 situation is difficult for all of us. In order to allow you to attend as many courses as possible and complete your study plan with minimum delays, we will try hard and will offer all courses in an online fashion.

Our plan is to make all lectures that will be taught in the summer term 2020 available as video lectures on YouTube. In addition to that, we will have question sessions via a live video conferencing system with the lecturer and the same for the tutorials. This might not be the perfect setup but the closest thing we can do to teaching in the lecture room.

We will start our video lecture early and aim at making all the material available in the beginning of April 2020 on our website. This will allow you to start taking our classes around the original schedule of the term and thus during a time when most of you will have to stay at home. We will be flexible w.r.t. the submission date for homework assignments.

This will affect all our courses, for the summer term 2020, this will:

  • Photogrammetry 1
  • Sensors and State Estimation 2
  • Modern C++
  • Master project (Stachniss – Kuhlmann – McCool)

The Master project course will be done using 1:1 supervision with regular live video supervision and will probably be a bit more structured compared to previous years. All in all, we will try to do our best to allow you to study your mandatory and elective courses with our lab in the summer term 2020.

All the best and stay healthy,
Cyrill Stachniss