Julia Nitsch — accepted AI-conference papers

2019

Object Classification Based on Unsupervised Learned Multi-Modal Features For Overcoming Sensor Failures

Julia Nitsch, Juan Nieto, Roland Siegwart, Max Schmidt, Cesar Cadena

ICRA 2019poster

For autonomous driving applications it is critical to know which type of road users and road side infrastructure are present to plan driving manoeuvres accordingly. Therefore autonomous cars are equipped with different sensor modalities to robustly perceive its environment. However, for classificati…

Cited by 4 Source ↗Scholar ↗

2017

Active Autonomous Aerial Exploration for Ground Robot Path Planning

Jeffrey A. Delmerico, Elias Mueggler, Julia Nitsch, Davide Scaramuzza

RA-L 2017

We address the problem of planning a path for a ground robot through unknown terrain, using observations from a flying robot. In search and rescue missions, which are our target scenarios, the time from arrival at the disaster site to the delivery of aid is critically important. Previous works requi

Cited by 136 Source ↗Scholar ↗