Evolutionary View Planning for Optimized UAV Terrain Modeling in a Simulated Environment

Martin, Ronald A.; Rojas, Ivan Yair; Franke, Kevin W.; Hedengren, John D.

doi:10.3390/rs8010026

Cited by 45 publications

(54 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Terragen 3, the environmental simulator used in the current work, has been used successfully by the authors in a previous paper, in which it was used to evaluate the performance of a genetic algorithm-based planner for UAV infrastructure modeling [36]. This approach differs from related work in the field through an increased level of fidelity in the simulation environment, including highly detailed vegetation models, realistic atmospheric effects, cloud cover, haze and fine control of camera, lens and lighting conditions.…”

Section: Simulationmentioning

confidence: 99%

See 1 more Smart Citation

Potential Benefits of Combining Anomaly Detection with View Planning for UAV Infrastructure Modeling

et al. 2017

Self Cite

View full text Add to dashboard Cite

This paper presents a novel method for UAV-based 3D modeling of large infrastructure objects, such as pipelines, canals and levees, that combines anomaly detection with automatic on-board 3D view planning. The study begins by assuming that anomaly detections are possible and focuses on quantifying the potential benefits of the combined method and the view planning algorithm. A simulated canal environment is constructed, and several simulated anomalies are created and marked. The algorithm is used to plan inspection flights for the anomaly locations, and simulated images from the flights are rendered and processed to construct 3D models of the locations of interest. The new flights are compared to traditional flights in terms of flight time, data collected and 3D model accuracy. When compared to a low speed, low elevation traditional flight, the proposed method is shown in simulation to decrease total flight time by up to 55%, while reducing the amount of image data to be processed by 89% and maintaining 3D model accuracy at areas of interest.

show abstract

Section: Simulationmentioning

confidence: 99%

“…Each model is evaluated for accuracy against the ground truth model using the techniques described in [36]. The models are aligned to the ground truth dataset using the open source software package CloudCompare [42].…”

Section: D Accuracy Testingmentioning

confidence: 99%

Potential Benefits of Combining Anomaly Detection with View Planning for UAV Infrastructure Modeling

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…A simulated imagery dataset using Terragen 3 was used validate an optimized flight plan methodology for UAS 3D reconstructions [25]. Numerous studies have been performed using the Rochester Institute of Technology's Digital Imaging and Remote Sensing Image Generation (DIRSIG) using for various active and passive sensors.…”

Section: Introductionmentioning

confidence: 99%

Simulated Imagery Rendering Workflow for UAS-Based Photogrammetric 3D Reconstruction Accuracy Assessments

Slocum

Parrish

2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:Structure from motion (SfM) and MultiView Stereo (MVS) algorithms are increasingly being applied to imagery from unmanned aircraft systems (UAS) to generate point cloud data for various surveying and mapping applications. To date, the options for assessing the spatial accuracy of the SfM-MVS point clouds have primarily been limited to empirical accuracy assessments, which involve comparisons against reference data sets, which are both independent and of higher accuracy than the data they are being used to test. The acquisition of these reference data sets can be expensive, time consuming, and logistically challenging. Furthermore, these experiments are also almost always unable to be perfectly replicated and can contain numerous confounding variables, such as sun angle, cloud cover, wind, movement of objects in the scene, and camera thermal noise, to name a few. The combination of these factors leads to a situation in which robust, repeatable experiments are cost prohibitive, and the experiment results are frequently site-specific and condition-specific. Here, we present a workflow to render computer generated imagery using a virtual environment which can mimic the independent variables that would be experienced in a real-world UAS imagery acquisition scenario. The resultant modular workflow utilizes Blender, an open source computer graphics software, for the generation of photogrammetrically-accurate imagery suitable for SfM processing, with explicit control of camera interior orientation, exterior orientation, texture of objects in the scene, placement of objects in the scene, and ground control point (GCP) accuracy. The challenges and steps required to validate the photogrammetric accuracy of computer generated imagery are discussed, and an example experiment assessing accuracy of an SfM derived point cloud from imagery rendered using a computer graphics workflow is presented. The proposed workflow shows promise as a useful tool for sensitivity analysis and SfM-MVS experimentation.

show abstract

“…In the VPP literature, greedy algorithm is still the most commonly used algorithm for view point selection [24,1]. There is a couple of recent work that uses more sophisticated methods such as linear programming relaxation and genetic algorithms for full 3D model coverage [5,12]. However, they don't necessarily suggest performance gains over the greedy algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Reinforcement Learning Approach to the View Planning Problem

Kaba

Uzunbaş

Lim

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

We present a Reinforcement Learning (RL) solution to the view planning problem (VPP), which generates a sequence of view points that are capable of sensing all accessible area of a given object represented as a 3D model. In doing so, the goal is to minimize the number of view points, making the VPP a class of set covering optimization problem (SCOP). The SCOP is N P -hard, and the inapproximability results tell us that the greedy algorithm provides the best approximation that runs in polynomial time. In order to find a solution that is better than the greedy algorithm, (i) we introduce a novel score function by exploiting the geometry of the 3D model, (ii) we model an intuitive human approach to VPP using this score function, and (iii) we cast VPP as a Markovian Decision Process (MDP), and solve the MDP in RL framework using well-known RL algorithms. In particular, we use SARSA, Watkins-Q and TD with function approximation to solve the MDP. We compare the results of our method with the baseline greedy algorithm in an extensive set of test objects, and show that we can outperform the baseline in almost all cases.

show abstract

Evolutionary View Planning for Optimized UAV Terrain Modeling in a Simulated Environment

Cited by 45 publications

References 55 publications

Potential Benefits of Combining Anomaly Detection with View Planning for UAV Infrastructure Modeling

Potential Benefits of Combining Anomaly Detection with View Planning for UAV Infrastructure Modeling

Simulated Imagery Rendering Workflow for UAS-Based Photogrammetric 3D Reconstruction Accuracy Assessments

A Reinforcement Learning Approach to the View Planning Problem

Contact Info

Product

Resources

About