Kategorie: Informatik

Publishing the „Carbonaro“ dataset from FRE 2022

Last year, we published our dataset and models for image recognition during the online Field Robot Event 2021. Our blog post ended with a call for cooperation in creating a dataset and AI models for this year’s event, which has since taken place at DLG Feldtage in June. Successful cooperation Team Carbonite from the Überlingen …

Getting ready for FRE2022: (non-virtual) testing maize field

As our name Kamaro (Karlsruhe maize robots) suggests we make maize and robots. While we have been obsessing about the robot part, we have been lacking maize as of late. Also, we were excited about testing our brand-new driving algorithm! So, we set about growing our very own maize field behind building 70.21. The most …

Deploying ResNet50 semantic segmentation model on the nVidia Jetson Nano using onnxruntime

When we upgraded our robot Beteigeuze with entirely new electronic components, we decided on nVidia’s Jetson Nano platform as the robot’s primary computer. The Jetson Nano is an ARM based quad-core System-on-a-Chip (SOC) that features CUDA acceleration for Deep Learning models. As a test, we tried deploying the model that we used during last year’s …

Publishing our Maize Row Navigation Algorithm

Following the release of our object detection deep learning code, model and dataset, we are now publishing the code we used for navigating within a crop row at the Field Robot Event. This code has now been in use at Kamaro for several years, and most recently won Task 1 at the competition in 2021.

Segmantic segmentation on a simulated maize field

Publishing our Object Detection Network and Dataset

In order to facilitate the use of deep learning based object recognition, and to improve cooperation and exchange between teams, we are open-sourcing the deep learning based weed recognition software that we used at this year’s online competition (FRE 2021).

Modulare Roboter mit RODOS – Beteigeuze NOVA

Abstract Kamaro Engineering e. V. forscht seit mehr als zehn Jahren an Feldrobotern und nimmt mit den entwickelten Prototypen an verschiedenen Wettbewerben teil. Beispielweise meistern die Roboter jährlich auf dem Field-Robot-Event neue Herausforderungen, die unterschiedlichste mechatronische Lösungen erfordern. Durch die von Event zu Event variierende Aufgabenstellung gewinnt eine modulare Bauweise mehr und mehr an Bedeutung, …

NDVI Mapping with the RealSense R200

A typical task in modern farming is the measurement of the state of plants. The Normalised Difference Vegetation Index is an index commonly used to determine plant health. It is defined as following: (NIR-Red) / (NIR+Red) Healthy plants have a NDVI value near one, vegetation free areas have a value near or below 0. The RealSense …

An image of the rviz visualization for graph slam.

Precise Localisation with Loop Closures using Graph SLAM

The Graph SLAM (Graph simultaneous localisation and mapping) algorithm plays a key role in our attempts to navigate autonomously in complex environments. Take an in-depth look at how it works.

Robotour 2015: Das Geheimnis wird gelüftet

Bei der Robotour 2015 musste Beteigeuze autonom eine Route von A nach B durch eine Stadt planen und dieser dann, durch enge Gassen und vorbei an zahlreichen Hindernissen, folgen. Der Herausforderung an die Roboter konnte Beteigeuze besser als die gesamte Konkurrenz meistern und so den 1. Platz belegen. Die Skills, die Beteigeuze den Sieg einbrachten, …

EKF-SLAM erklärt: Wie sieht ein Roboter die Welt?

Um eine detaillierte Karte der Umgebung zu erstellen, setzten wir auf einen EKF-SLAM Algorithmus. (Extendes Kalman Filter – Simultaneous Localization and Mapping) Die Kartenerstellung mit EKF-SLAM ist eine Möglichkeit für Roboter, die Welt zu sehen. Um zu verstehen was das ist, sollte man jedoch zunächst einige Grundlagen betrachten. Unser Roboter tastet mit zwei LIDAR-Scannern seine …