Probabilistic view planner for 3D modelling indoor environments

Lopez-Damian, Efrain; Etcheverry, Gibran; Sucar, L. Enrique

doi:10.1109/iros.2009.5354375

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Information gain has widely been used as viewpoint selection criterion in classical 3D modelling [20,21]. Newer approaches compute the expected information gain using a discrete approximation of the sensor's field of view based on pixel's ray-tracing techniques.…”

Section: Antecedentsmentioning

confidence: 99%

Task-driven active sensing framework applied to leaf probing

Foix

Alenyà

Torras

2018

Computers and Electronics in Agriculture

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This article presents a new method for actively exploring a 3D workspace with the aim of localizing relevant regions for a given task. Our method encodes the exploration route in a multi-layer occupancy grid map. This map, together with a multiple-view estimator and a maximum-information-gain gathering approach, incrementally provide a better understanding of the scene until reaching the task termination criterion. This approach is designed to be applicable to any task entailing 3D object exploration where some previous knowledge of its approximate shape is available. Its suitability is demonstrated here for a leaf probing task using an eye-in-hand arm configuration in the context of a phenotyping application (leaf probing).

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Section: Antecedentsmentioning

confidence: 99%

Task-driven active sensing framework applied to leaf probing

Foix

Alenyà

Torras

2018

Computers and Electronics in Agriculture

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“…For their part, Blaer et al take into account the number of borderline voxels, that is unknown voxels with an empty neighbor [ 16 ]. Lopez et al [ 10 ] use, among other criteria, a limit for the number of the so-called occplane voxels (occluded voxel with at least one empty neighbor).…”

Section: The Following Scansmentioning

confidence: 99%

“…A 3D NBV algorithm for the reconstruction of indoor environments with polynomial functions in a voxelized space is proposed in [ 9 ]. An extended version of this is introduced by Lopez–Damian et al by using probabilistic functions [ 10 ]. They evaluated the efficiency of their algorithms on simulated indoor scenes.…”

Section: Introductionmentioning

confidence: 99%

Towards the Automatic Scanning of Indoors with Robots

Adán

Quintana

Vázquez

et al. 2015

Sensors

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This paper is framed in both 3D digitization and 3D data intelligent processing research fields. Our objective is focused on developing a set of techniques for the automatic creation of simple three-dimensional indoor models with mobile robots. The document presents the principal steps of the process, the experimental setup and the results achieved. We distinguish between the stages concerning intelligent data acquisition and 3D data processing. This paper is focused on the first stage. We show how the mobile robot, which carries a 3D scanner, is able to, on the one hand, make decisions about the next best scanner position and, on the other hand, navigate autonomously in the scene with the help of the data collected from earlier scans. After this stage, millions of 3D data are converted into a simplified 3D indoor model. The robot imposes a stopping criterion when the whole point cloud covers the essential parts of the scene. This system has been tested under real conditions indoors with promising results. The future is addressed to extend the method in much more complex and larger scenarios.

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“…To achieve correct modelling from the 3D information of an interior environment [12][13][14][15][16][17] the occluded space must be minimized.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Point Cloud Acquisition of Unknown Indoor Scenes

González-deSantos

Díaz-Vilariño

Balado

et al. 2018

IJGI

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This paper presents a methodology for the automatic selection of heuristic scanning positions in unknown indoor environments. The surveying is carried out by a robotic system following a stop-and-go procedure. Starting with a random scan position in the room, the point cloud is discretized in voxels and they are submitted to a two-step classification and are labelled as occupied, occluded, empty, window, door, or exterior based on a visibility analysis. The main objective of the methodology is to obtain a complete point cloud of the indoor space and accordingly, the next best position is the scan position minimizing occluded voxels. Because the method locates doors and windows, the room can be delimited and the scan can continue for adjacent rooms. This approach has been tested in a real case study, in which three scans were developed.

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Probabilistic view planner for 3D modelling indoor environments

Cited by 3 publications

References 13 publications

Task-driven active sensing framework applied to leaf probing

Task-driven active sensing framework applied to leaf probing

Towards the Automatic Scanning of Indoors with Robots

Autonomous Point Cloud Acquisition of Unknown Indoor Scenes

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