Jan Gutsche

Hamburg University | UHH · Department of Informatics

Robotics researchers have been focusing on developing autonomous and human-like intelligent robots that are able to plan, navigate, manipulate objects, and interact with humans in both static and dynamic environments. These capabilities, however, are usually developed for direct interactions with people in controlled environments, and evaluated pri...

We present a dataset specifically designed to be used as a benchmark to compare vision systems in the RoboCup Humanoid Soccer domain. The dataset is composed of a collection of images taken in various real-world locations as well as a collection of simulated images. It enables comparing vision approaches with a meaningful and expressive metric. The...

Fast and accurate visual perception utilizing a robot's limited hardware resources is necessary for many mobile robot applications. We are presenting YOEO, a novel hybrid CNN which unifies previous object detection and semantic segmentation approaches using one shared encoder backbone to increase performance and accuracy. We show that it outperform...

We present a dataset specifically designed to be used as a benchmark to compare vision systems in the RoboCup Humanoid Soccer domain. The dataset is composed of a collection of images taken in various real-world locations as well as a collection of simulated images. It enables comparing vision approaches with a meaningful and expressive metric. The...

This extended abstract describes the current research of the Hamburg Bit-Bots Humanoid KidSize RoboCup team, lessons learned from last years competition and improvements planned for 2020. In the RoboCup 2019 competition, we had recently installed new cameras on the robots with a new 3D printed head. These heads sometimes broke when the robot fell....

We are proposing an Open Source ROS vision pipeline for the RoboCup Soccer context. It is written in Python and offers sufficient precision while running with an adequate frame rate on the hardware of kid-sized humanoid robots to allow a fluent course of the game. Fully Convolutional Neural Networks (FCNNs) are used to detect balls while convention...

We are proposing an Open Source ROS vision pipeline for the RoboCup Soccer context. It is written in Python and offers sufficient precision while running with an adequate frame rate on the hardware of kid-sized humanoid robots to allow a fluent course of the game. Fully Convolutional Neural Networks (FCNNs) are used to detect balls while convention...