arXiv subjects

Federico Magistri

Publications and source records attributed to Federico Magistri.

22 records · Page 2Linked to original sources

arXiv2022

Informative Path Planning for Active Learning in Aerial Semantic Mapping

Semantic segmentation of aerial imagery is an important tool for mapping and earth observation. However, supervised deep learning models for segmentation rely on large amounts of high-quality labelled data, which is labour-intensive and time-consuming to generate. To address this, we propose a new approach for using unmanned aerial vehicles (UAVs) to autonomously collect useful data for model training. We exploit a Bayesian approach to estimate model uncertainty in semantic segmentation. During a mission, the semantic predictions and model uncertainty are used as input for terrain mapping. A key aspect of our pipeline is to link the mapped model uncertainty to a robotic planning objective based on active learning. This enables us to adaptively guide a UAV to gather the most informative terrain images to be labelled by a human for model training. Our experimental evaluation on real-world data shows the benefit of using our informative planning approach in comparison to static coverage paths in terms of maximising model performance and reducing labelling efforts.

cs.RO↗

arXiv2022

Monitoring and mapping of crop fields with UAV swarms based on information gain

Monitoring crop fields to map features like weeds can be efficiently performed with unmanned aerial vehicles (UAVs) that can cover large areas in a short time due to their privileged perspective and motion speed. However, the need for high-resolution images for precise classification of features (e.g., detecting even the smallest weeds in the field) contrasts with the limited payload and ight time of current UAVs. Thus, it requires several flights to cover a large field uniformly. However, the assumption that the whole field must be observed with the same precision is unnecessary when features are heterogeneously distributed, like weeds appearing in patches over the field. In this case, an adaptive approach that focuses only on relevant areas can perform better, especially when multiple UAVs are employed simultaneously. Leveraging on a swarm-robotics approach, we propose a monitoring and mapping strategy that adaptively chooses the target areas based on the expected information gain, which measures the potential for uncertainty reduction due to further observations. The proposed strategy scales well with group size and leads to smaller mapping errors than optimal pre-planned monitoring approaches.

cs.RO↗

arXiv2022

Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation

Efficient data collection methods play a major role in helping us better understand the Earth and its ecosystems. In many applications, the usage of unmanned aerial vehicles (UAVs) for monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. A key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. This is, for example, relevant for monitoring agricultural fields. This paper addresses the problem of adaptive path planning for accurate semantic segmentation of using UAVs. We propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas with fine details as they are detected in incoming images. This enables us to perform close inspections at low altitudes only where required, without wasting energy on exhaustive mapping at maximum image resolution. A key feature of our approach is a new accuracy model for deep learning-based architectures that captures the relationship between UAV altitude and semantic segmentation accuracy. We evaluate our approach on different domains using real-world data, proving the efficacy and generability of our solution.

cs.CV↗

arXiv2021

Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation

In this paper, we address the problem of adaptive path planning for accurate semantic segmentation of terrain using unmanned aerial vehicles (UAVs). The usage of UAVs for terrain monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. However, a key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. To address this, we propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas on the terrain with fine details as they are detected in incoming images. This enables us to perform close inspections at low altitudes only where required, without wasting energy on exhaustive mapping at maximum resolution. A key feature of our approach is a new accuracy model for deep learning-based architectures that captures the relationship between UAV altitude and semantic segmentation accuracy. We evaluate our approach on the application of crop/weed segmentation in precision agriculture using real-world field data.

cs.RO↗