285 citations · 661 across the 7 of their papers we have counts for
11 papers
OverlapNet: Loop Closing for LiDAR-based SLAM
Xieyuanli Chen, Thomas Läbe, Andres Milioto +5
Simultaneous localization and mapping (SLAM) is a fundamental capability required by most autonomous systems. In this paper, we address the problem of loop closing for SLAM based o…
A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI
Jens Behley, Andres Milioto, Cyrill Stachniss
Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly. In this paper, we present an extension of SemanticKITTI,…
Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
Alberto Pretto, Stéphanie Aravecchia, Wolfram Burgard +25
The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency…
Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks
Laura Zabawa, Anna Kicherer, Lasse Klingbeil +4
Yield estimation and forecasting are of special interest in the field of grapevine breeding and viticulture. The number of harvested berries per plant is strongly correlated with t…
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Jens Behley, Martin Garbade, Andres Milioto +4
Semantic scene understanding is important for various applications. In particular, self-driving cars need a fine-grained understanding of the surfaces and objects in their vicinity…
Geometrical Stem Detection from Image Data for Precision Agriculture
F. Langer, L. Mandtler, A. Milioto +2
High efficiency in precision farming depends on accurate tools to perform weed detection and mapping of crops. This allows for precise removal of harmful weeds with a lower amount…
Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming
Philipp Lottes, Jens Behley, Nived Chebrolu +2
Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat ev…
Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming
Philipp Lottes, Jens Behley, Andres Milioto +1
Reducing the use of agrochemicals is an important component towards sustainable agriculture. Robots that can perform targeted weed control offer the potential to contribute to this…
Ocean Eddy Identification and Tracking using Neural Networks
Katharina Franz, Ribana Roscher, Andres Milioto +2
Global climate change plays an essential role in our daily life. Mesoscale ocean eddies have a significant impact on global warming, since they affect the ocean dynamics, the energ…
Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs
Andres Milioto, Cyrill Stachniss
The ability to interpret a scene is an important capability for a robot that is supposed to interact with its environment. The knowledge of what is in front of the robot is, for ex…
Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
Andres Milioto, Philipp Lottes, Cyrill Stachniss
Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time…