Project Details
Projekt
IP2: Exploiting Repeated Data Acquisitions for Improved Long-term Monitoring Capabilities
Applicant
Professor Dr. Cyrill Stachniss
Subject Area
Plant Cultivation, Plant Nutrition, Agricultural Technology
Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 459376902
In the agricultural domain, fields as well as orchards are monitored repeatedly to assess the status quo and to trigger management decisions. To assess growth stages or compute phenotypic parameters of plants, knowledge about the plant geometry and further semantic information is key. Thus, estimating 3D geometric and semantic models of plants plays a key role in the automated status quo assessment. Most sensor-based monitoring systems, however, assume that the sensor platform is observing a new scene whenever starting the data acquisition process. Few approaches take prior maps from previous data into account to extend models or automatically track changes such as growth over time. Often, the fact that the same scene/objects are re-observed, potentially after undergoing some changes, is not exploited to its full extent. This project aims at addressing this challenge and will answer the following three coupled aspects: ``How to build accurate plant models and exploit the fact that the same, but growing and changing objects, are being monitored repeatedly to (i) improve and achieve consistent modeling in the spatial and temporal dimensions (4D), (ii) estimate semantic information more precisely and consistently over time, and (iii) improve the involved learning approaches in a self-supervised or unsupervised way by exploiting prior knowledge about the scene?'' This project will develop new approaches and will extend current systems for robot mapping/SLAM, filtering approaches for dealing with change, as well as contrastive learning in combination with deep neural networks to tackle the three research questions.
DFG Programme
Research Units
