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

• URL: http://arxiv.org/abs/2108.01884v1
• Date: Wed, 4 Aug 2021 07:30:04 GMT
• Title: Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation
• Authors: Felix Stache and Jonas Westheider and Federico Magistri and Marija Popovi\'c and Cyrill Stachniss
• Abstract summary: We propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations. A key feature of our approach is a new accuracy model for deep learning-based architectures. We evaluate our approach on the application of crop/weed segmentation in precision agriculture using real-world field data.
• Score: 26.729010176211016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Automatic UAV-based Airport Pavement Inspection Using Mixed Real and Virtual Scenarios [3.0874677990361246]
  We propose a vision-based approach to automatically identify pavement distress using images captured by UAVs. The proposed method is based on Deep Learning (DL) to segment defects in the image. We demonstrate that the use of a mixed dataset composed of synthetic and real training images yields better results when testing the training models in real application scenarios.
  arXiv  Detail & Related papers  (2024-01-11T16:30:07Z)
• An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping [27.460481202195012]
  Unmanned aerial vehicles (UAVs) are frequently used for aerial mapping and general monitoring tasks. Recent progress in deep learning enabled automated semantic segmentation of imagery to facilitate the interpretation of large-scale complex environments. We propose a novel general planning framework for UAVs to autonomously acquire informative training images for model re-training.
  arXiv  Detail & Related papers  (2023-02-07T09:41:21Z)
• Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation [28.104584236205405]
  A key challenge is planning missions to maximize the value of acquired data in large environments. This is, for example, relevant for monitoring agricultural fields. We propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations.
  arXiv  Detail & Related papers  (2022-03-03T11:03:28Z)
• Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization [79.96177511319713]
  We invoke deep learning (DL) to assist routing in aeronautical ad-hoc networks (AANETs) A deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. We extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime.
  arXiv  Detail & Related papers  (2021-10-28T14:18:22Z)
• Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy [48.51396198176273]
  We propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments. We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models. A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability.
  arXiv  Detail & Related papers  (2021-09-14T07:24:53Z)
• Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images [93.50240389540252]
  Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. We propose a novel stagewise domain adaptation model called RoadDA to address the domain shift (DS) issue in this field. Experiment results on two benchmarks demonstrate that RoadDA can efficiently reduce the domain gap and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-28T09:29:14Z)
• Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach [93.67588414950656]
  In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a 3D environment. We present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.
  arXiv  Detail & Related papers  (2021-07-23T03:33:29Z)
• UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning [16.720630804675213]
  This work presents a method for autonomous UAV path planning based on deep reinforcement learning (DRL) We compare coverage path planning ( CPP), where the UAV's goal is to survey an area of interest to data harvesting (DH), where the UAV collects data from distributed Internet of Things (IoT) sensor devices. By exploiting structured map information of the environment, we train double deep Q-networks (DDQNs) with identical architectures on both distinctly different mission scenarios.
  arXiv  Detail & Related papers  (2020-10-14T09:59:10Z)
• Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection [63.942632088208505]
  We propose a post-processing algorithm to align the segmented plane masks with edges detected in the image. This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects.
  arXiv  Detail & Related papers  (2020-03-28T18:51:43Z)
• Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach [88.45509934702913]
  We design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed. We incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time.
  arXiv  Detail & Related papers  (2020-02-21T07:29:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.