Dominik Brämer

Piet Brömmel, Dominik Brämer, Oliver Urbann et al.

The localization of moving robots depends on the availability of good features from the environment. Sensor systems like Lidar are popular, but unique features can also be extracted from images of the ground. This work presents the Keypoint Localization Framework (KOALA), which utilizes deep neural networks that extract sufficient features from an industrial floor for accurate localization without having readable markers. For this purpose, we use a floor covering that can be produced as cheaply as common industrial floors. Although we do not use any filtering, prior, or temporal information, we can estimate our position in 75.7 % of all images with a mean position error of 2 cm and a rotation error of 2.4 %. Thus, the robot kidnapping problem can be solved with high precision in every frame, even while the robot is moving. Furthermore, we show that our framework with our detector and descriptor combination is able to outperform comparable approaches.

Dominik Brämer, Diana Kleingarn, Oliver Urbann

Accurate localization represents a fundamental challenge in robotic navigation. Traditional methodologies, such as Lidar or QR-code based systems, suffer from inherent scalability and adaptability con straints, particularly in complex environments. In this work, we propose an innovative localization framework that harnesses flooring characteris tics by employing graph-based representations and Graph Convolutional Networks (GCNs). Our method uses graphs to represent floor features, which helps localize the robot more accurately (0.64cm error) and more efficiently than comparing individual image features. Additionally, this approach successfully addresses the kidnapped robot problem in every frame without requiring complex filtering processes. These advancements open up new possibilities for robotic navigation in diverse environments.