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

Stache, Felix; Westheider, Jonas; Magistri, Federico; Popović, Marija; Stachniss, Cyrill

doi:10.1109/ecmr50962.2021.9568788

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…This article corresponds to an extension of the authors' preliminary conference work [35]. We consolidate our previous contribution in adaptive planning with additional explanations and experimental results.…”

Section: Introductionsupporting

confidence: 53%

“…By modifying plans online, such strategies enable adapting the flight path according to the mission aim to maximize the value of collected data. This can be used for improving the geometric estimation [22] or for semantic estimation tasks [35,5]. Several previous studies in UAV-based informative planning either consider planning at a fixed altitude [37,31], i.e.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation

Stache¹,

Westheider²,

Magistri³

et al. 2022

Preprint

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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.

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Section: Introductionsupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation

Stache¹,

Westheider²,

Magistri³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…3) Application: Geometric criteria like completeness and accuracy of the 3-D models acquired from UAV missions are the most interesting properties discussed in the literature [45]. However, researchers also aimed at other properties like semantic information, which could be extracted from the UAV data [73], [74], [75]. Apart from many other applications like precision agriculture [76], [77], [78], indoor exploration and reconstruction [79], [80], [81], [82], [83] to name a few, we focus on viewpoints and path planning for 3-D reconstruction of complex outdoor man-made objects like buildings and bridges which is a topic of interest for many applications [84], [85], [86].…”

Section: A Scope Of the Papermentioning

confidence: 99%

A Review on Viewpoints and Path Planning for UAV-Based 3-D Reconstruction

Maboudi

Homaei

Song³

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Unmanned aerial vehicles (UAVs) are widely used platforms to carry data capturing sensors for various applications. The reason for this success can be found in many aspects: the high maneuverability of the UAVs, the capability of performing autonomous data acquisition, flying at different heights, and the possibility to reach almost any vantage point. The selection of appropriate viewpoints and planning the optimum trajectories of UAVs is an emerging topic that aims at increasing the automation, efficiency, and reliability of the data capturing process to achieve a dataset with desired quality. On the other hand, 3-D reconstruction using the data captured by UAVs is also attracting attention in research and industry. This article investigates a wide range of model-free and model-based algorithms for viewpoints and path planning for 3-D reconstruction of large-scale objects. It presents a bibliography of more than 200 references to cover different aspects of the topic. The analyzed approaches are limited to those that employ a single-UAV as a data capturing platform for outdoor 3-D reconstruction purposes. In addition to discussing the evaluation strategies, this article also highlights the innovations and limitations of the investigated approaches. It concludes with a critical analysis of the existing challenges and future research perspectives.

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