Surfel-Based Next Best View Planning

Monica, Riccardo; Aleotti, Jacopo

doi:10.1109/lra.2018.2852778

Cited by 23 publications

(9 citation statements)

References 23 publications

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“…Recent segmentation algorithms have enabled robust classification of partially observed objects in cluttered environments [5], [11], [12], [13]; we leverage Gaussian process implicit surface (GPIS) segmentation [5] to detect ROIs and estimate the value of future observations. The GPIS approach "hallucinates" unseen parts of the scene to make predictions; related prediction models that could also potentially be used within our framework include [14], [15], [16].…”

Section: Related Workmentioning

confidence: 99%

Multi-Robot Region-of-Interest Reconstruction with Dec-MCTS

Sukkar

Best

Yoo

2019

2019 International Conference on Robotics and Automation (ICRA)

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Section: Related Workmentioning

confidence: 99%

Multi-Robot Region-of-Interest Reconstruction with Dec-MCTS

Sukkar

Best

Yoo

2019

2019 International Conference on Robotics and Automation (ICRA)

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“…Karaszewski et al (2016) evaluate and compare different algorithms for pose planning for this application. A current example of such an algorithm is the work of Monica and Aleotti (2018). Their approach is based on the representation of the environment by surface elements.…”

Section: Positioning Of Sensorsmentioning

confidence: 99%

Simulation-based layout optimization for multi-station assembly lines

et al. 2021

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This article presents a novel approach for the automated 3D-layout planning of multi-station assembly lines. The planning method is based on a comprehensive model of the used production resources, including their geometry, kinematic properties, and general characteristics. Different resource types can be included in the planning system. A genetic algorithm generates and optimizes possible layouts for a line. The optimization aims to minimize the line’s area and the costs for assembling the line while simultaneously optimizing the resources’ positioning to perform their tasks. The line’s cycle time is considered as a boundary condition. For the evaluation of different layout alternatives, a multi-body simulation is performed. A parameter study is used to set the algorithm’s parameters. Afterward, the algorithm is applied to three increasingly complex examples to validate and evaluate its functionality. The approach is promising for industrial applications as it allows the integration of various resource types and individualization of the optimization function.

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“…Based on the rapid development of computer vision algorithms and hardware upgrade, the non-model-based methods are further divided into the surface based and the volumetric based. For the surface-based approaches, the Next Best View is estimated in the form of the frontier voxels as described by Yamauchi [29], and latest by Quin et al [30] and Monica and Aleotti [31] for unknown space exploration. Although these approaches can directly work on surface patches which compose the final output of the triangle mesh with high efficiency, the image processing and the visibility modeling can be rather complex, e.g.…”

Section: Related Workmentioning

confidence: 99%

Volumetric Next Best View by 3D Occupancy Mapping Using Markov Chain Gibbs Sampler for Precise Manufacturing

et al. 2019

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In this paper, we propose a model-free volumetric Next Best View (NBV) algorithm for accurate 3D reconstruction using a Markov Chain Monte Carlo method for high-mix-low-volume objects in manufacturing. The volumetric information gain based Next Best View algorithm can in real-time select the next optimal view that reveals the maximum uncertainty of the scanning environment with respect to a partially reconstructed 3D Occupancy map, without any priori knowledge of the target. Traditional Occupancy grid maps make two independence assumptions for computational tractability but suffer from the overconfident estimation of the occupancy probability for each voxel leading to less precise surface reconstructions. This paper proposes a special case of the Markov Chain Monte Carlo (MCMC) method, the Gibbs sampler, to accurately estimate the posterior occupancy probability of a voxel by randomly sampling from its high-dimensional full posterior occupancy probability given the entire volumetric map with respect to the forward sensor model with a Gaussian distribution. Numerical experiments validate the performance of the MCMC Gibbs sampler algorithm under the ROS-Industry framework to prove the accuracy of the reconstructed Occupancy map and the completeness of the registered point cloud. The proposed MCMC Occupancy mapping could be used to optimise the tuning parameters of the online NBV algorithms via the inverse sensor model to realise industry automation.INDEX TERMS Active vision, Markov chain Monte Carlo, occupancy mapping, 3D reconstruction, viewpoint planning.

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Surfel-Based Next Best View Planning

Cited by 23 publications

References 23 publications

Multi-Robot Region-of-Interest Reconstruction with Dec-MCTS

Multi-Robot Region-of-Interest Reconstruction with Dec-MCTS

Simulation-based layout optimization for multi-station assembly lines

Volumetric Next Best View by 3D Occupancy Mapping Using Markov Chain Gibbs Sampler for Precise Manufacturing

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