94 research outputs found
Multiresolution mapping and informative path planning for UAV-based terrain monitoring
© 2017 IEEE. Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery of high-quality sensing data. However, deciding when and where to take measurements in complex environments remains an open challenge. To address this issue, we introduce a new multiresolution mapping approach for informative path planning in terrain monitoring using UAVs. Our strategy exploits the spatial correlation encoded in a Gaussian Process model as a prior for Bayesian data fusion with probabilistic sensors. This allows us to incorporate altitude-dependent sensor models for aerial imaging and perform constant-time measurement updates. The resulting maps are used to plan information-rich trajectories in continuous 3-D space through a combination of grid search and evolutionary optimization. We evaluate our framework on the application of agricultural biomass monitoring. Extensive simulations show that our planner performs better than existing methods, with mean error reductions of up to 45% compared to traditional 'lawnmower' coverage. We demonstrate proof of concept using a multirotor to map color in different environments
Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search
Target search with unmanned aerial vehicles (UAVs) is relevant problem to
many scenarios, e.g., search and rescue (SaR). However, a key challenge is
planning paths for maximal search efficiency given flight time constraints. To
address this, we propose the Obstacle-aware Adaptive Informative Path Planning
(OA-IPP) algorithm for target search in cluttered environments using UAVs. Our
approach leverages a layered planning strategy using a Gaussian Process
(GP)-based model of target occupancy to generate informative paths in
continuous 3D space. Within this framework, we introduce an adaptive replanning
scheme which allows us to trade off between information gain, field coverage,
sensor performance, and collision avoidance for efficient target detection.
Extensive simulations show that our OA-IPP method performs better than
state-of-the-art planners, and we demonstrate its application in a realistic
urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and
Automation (ICRA-2019) to be held at Montreal, Canad
An informative path planning framework for UAV-based terrain monitoring
© 2020, The Author(s). Unmanned aerial vehicles represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path planning framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori. The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and a proof of concept for an agricultural monitoring task
An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping
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. Commonly used supervised deep learning for
segmentation relies on large amounts of pixel-wise labelled data, which is
tedious and costly to annotate. The domain-specific visual appearance of aerial
environments often prevents the usage of models pre-trained on publicly
available datasets. To address this, we propose a novel general planning
framework for UAVs to autonomously acquire informative training images for
model re-training. We leverage multiple acquisition functions and fuse them
into probabilistic terrain maps. Our framework combines the mapped acquisition
function information into the UAV's planning objectives. In this way, the UAV
adaptively acquires informative aerial images to be manually labelled for model
re-training. Experimental results on real-world data and in a photorealistic
simulation show that our framework maximises model performance and drastically
reduces labelling efforts. Our map-based planners outperform state-of-the-art
local planning.Comment: 18 pages, 24 figure
Informative Path Planning for Active Field Mapping under Localization Uncertainty
Information gathering algorithms play a key role in unlocking the potential
of robots for efficient data collection in a wide range of applications.
However, most existing strategies neglect the fundamental problem of the robot
pose uncertainty, which is an implicit requirement for creating robust,
high-quality maps. To address this issue, we introduce an informative planning
framework for active mapping that explicitly accounts for the pose uncertainty
in both the mapping and planning tasks. Our strategy exploits a Gaussian
Process (GP) model to capture a target environmental field given the
uncertainty on its inputs. For planning, we formulate a new utility function
that couples the localization and field mapping objectives in GP-based mapping
scenarios in a principled way, without relying on any manually tuned
parameters. Extensive simulations show that our approach outperforms existing
strategies, with reductions in mean pose uncertainty and map error. We also
present a proof of concept in an indoor temperature mapping scenario.Comment: 8 pages, 7 figures, submission (revised) to Robotics & Automation
Letters (and IEEE International Conference on Robotics and Automation
Development of Neural Network Based Adaptive Change Detection Technique for Land Terrain Monitoring with Satellite and Drone Images
Role of satellite images is increasing in day-to-day life for both civil as well as defence applications. One of the major defence application while troop’s movement is to know about the behaviour of the terrain in advance by which smooth transportation of the troops can be made possible. Therefore, it is important to identify the terrain in advance which is quite possible with the use of satellite images. However, to achieve accurate results, it is essential that the data used should be precise and quite reliable. To achieve this with a satellite image alone is a challenging task. Therefore, in this paper an attempt has been made to fuse the images obtained from drone and satellite, to achieve precise terrain information like bare land, dense vegetation and sparse vegetation. For this purpose, a test area nearby Roorkee, Uttarakhand, India has been selected, and drone and Sentinel-2 data have been taken for the same dates. A neural network based technique has been proposed to obtain precise terrain information from the Sentinel-2 image. A quantitative analysis was carried out to know the terrain information by using change detection. It is observed that the proposed technique has a good potential to identify precisely bare land, dense vegetation, and sparse vegetation which may be quite useful for defence as well as civilian application
Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
[No abstract available
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