Design and development of a fast and precise low-cost 3D laser rangefinder

Morales, José; Martínez, Jorge L.; Mandow, Anthony; Pequeño-Boter, Alejandro; García-Cerezo, Alfonso J.

doi:10.1109/icmech.2011.5971190

Cited by 35 publications

(35 citation statements)

References 10 publications

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“…For this purpose, two natural terrains have been scanned (see Fig. 3) with a 3D rangefinder [27] built by pitching a Hokuyo UTM-30LX 2D rangefinder whose maximum range is 30 m.…”

Section: Resultsmentioning

confidence: 99%

Navigability analysis of natural terrains with fuzzy elevation maps from ground-based 3D range scans

Martínez

Mandow

Reina

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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-Mobile robot navigation through natural terrains is a challenging issue with applications such as planetary exploration or search and rescue. This paper proposes navigability assessment of natural terrains scanned from groundbased 3D laser rangefinders. A continuous model of the terrain is obtained as a fuzzy elevation map (FEM). Based on this model, the proposed solution incorporates terrain navigability both in terms of uncertainties of the 3D input data and slope of the fuzzy surface. Moreover, the paper discusses the application of this method for local path planning. For this purpose, the Bug algorithm has been adapted to compute local paths on the navigable region of the FEM. The method has been applied to actual 3D point clouds on two different experimental sites. Abstract-Mobile robot navigation through natural terrains is a challenging issue with applications such as planetary exploration or search and rescue. This paper proposes navigability assessment of natural terrains scanned from ground-based 3D laser rangefinders. A continuous model of the terrain is obtained as a fuzzy elevation map (FEM). Based on this model, the proposed solution incorporates terrain navigability both in terms of uncertainties of the 3D input data and slope of the fuzzy surface. Moreover, the paper discusses the application of this method for local path planning. For this purpose, the Bug algorithm has been adapted to compute local paths on the navigable region of the FEM. The method has been applied to actual 3D point clouds on two different experimental sites.

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“…For this purpose, two natural terrains have been scanned (see Fig. 3) with a 3D rangefinder [27] built by pitching a Hokuyo UTM-30LX 2D rangefinder whose maximum range is 30 m.…”

Section: Resultsmentioning

confidence: 99%

Navigability analysis of natural terrains with fuzzy elevation maps from ground-based 3D range scans

Martínez

Mandow

Reina

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…For this experiment we use a part of the scene with 41 big objects including buildings, trees, cars, bushes, fences and light poles. The other two point clouds show a natural environment in Malaga captured by a low-cost laser range finder called Unolaser [15]. One of them represents a dense forest with many trees and bushes over the ground and the other contains a steep slope and just a tree and a bush.…”

Section: Discussionmentioning

confidence: 99%

“…The Figure 1 shows a dense point cloud modelled as a GFV map. The scene shows a natural environment in Malaga captured by a low-cost laser range finder called Unolaser [15].…”

Section: A Geometric-featured Voxel Mapmentioning

confidence: 99%

3D segmentation method for natural environments based on a Geometric-Featured Voxel map

Plaza

Ababsa

García-Cerezo

et al. 2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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-This work proposes a new segmentation algorithm for three-dimensional dense point clouds and has been specially designed for natural environments where the ground is unstructured and may include big slopes, non-flat areas and isolated areas. This technique is based on a Geometric-Featured Voxel map (GFV) where the scene is discretized in constant size cubes or voxels which are classified in flat surface, linear or tubular structures and scattered or undefined shapes, usually corresponding to vegetation. Since this is not a point-based technique the computational cost is significantly reduced, hence it may be compatible with Real-Time applications. The ground is extracted in order to obtain more accurate results in the posterior segmentation process. The scene is split into objects and a second segmentation in regions inside each object is performed based on the voxel's geometric class. The work here evaluates the proposed algorithm in various versions and several voxel sizes and compares the results with other methods from the literature. For the segmentation evaluation the algorithms are tested on several differently challenging hand-labeled data sets using two metrics, one of which is novel. 3D Segmentation Method for Natural Environments based on a Geometric-Featured Voxel MapVictoria Plaza * , Fakhr-Eddine Ababsa † , Alfonso J. Garcia-Cerezo * and Jose Antonio Gomez-Ruiz * * University of Malaga, 29071 Malaga, Spain. Email: victoriaplaza, ajgarcia, janto@uma.es † Evry University, IBISC EA 4526 France Email: fakhr-eddine.ababsa@univ-evry.frAbstract-This work proposes a new segmentation algorithm for three-dimensional dense point clouds and has been specially designed for natural environments where the ground is unstructured and may include big slopes, non-flat areas and isolated areas. This technique is based on a Geometric-Featured Voxel map (GFV) where the scene is discretized in constant size cubes or voxels which are classified in flat surface, linear or tubular structures and scattered or undefined shapes, usually corresponding to vegetation. Since this is not a point-based technique the computational cost is significantly reduced, hence it may be compatible with Real-Time applications. The ground is extracted in order to obtain more accurate results in the posterior segmentation process. The scene is split into objects and a second segmentation in regions inside each object is performed based on the voxel's geometric class. The work here evaluates the proposed algorithm in various versions and several voxel sizes and compares the results with other methods from the literature. For the segmentation evaluation the algorithms are tested on several differently challenging hand-labeled data sets using two metrics, one of which is novel.

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“…3 Range images have been obtained with a 3D laser scanner built by pitching a Hokuyo UTM-30LX 2D rangefinder [30] -see Fig. 3(right).…”

Section: Methodsmentioning

confidence: 99%

Building Fuzzy Elevation Maps from a Ground-Based 3D Laser Scan for Outdoor Mobile Robots

Mandow

Cantador

Reina

et al. 2015

Advances in Intelligent Systems and Computing

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-The paper addresses terrain modeling for mobile robots with fuzzy elevation maps by improving computational speed and performance over previous work on fuzzy terrain identification from a three-dimensional (3D) scan. To this end, spherical sub-sampling of the raw scan is proposed to select training data that does not filter out salient obstacles. Besides, rule structure is systematically defined by considering triangular sets with an unevenly distributed standard fuzzy partition and zero order Sugeno-type consequents. This structure, which favors a faster training time and reduces the number of rule parameters, also serves to compute a fuzzy reliability mask for the continuous fuzzy surface. The paper offers a case study using a Hokuyo-based 3D rangefinder to model terrain with and without outstanding obstacles. Performance regarding error and model size is compared favorably with respect to a solution that uses quadric-based surface simplification (QSlim). Keywords: Elevation maps, mobile robots, 3D scanners, fuzzy modeling ___________________________________________________________________________________________________This is a self-archiving copy of the author's accepted manuscript. The final publication is available at Springer via http://link.springer.com/book/10.1007/978-3-319-27149-1. Abstract. The paper addresses terrain modeling for mobile robots with fuzzy elevation maps by improving computational speed and performance over previous work on fuzzy terrain identification from a threedimensional (3D) scan. To this end, spherical sub-sampling of the raw scan is proposed to select training data that does not filter out salient obstacles. Besides, rule structure is systematically defined by considering triangular sets with an unevenly distributed standard fuzzy partition and zero order Sugeno-type consequents. This structure, which favors a faster training time and reduces the number of rule parameters, also serves to compute a fuzzy reliability mask for the continuous fuzzy surface. The paper offers a case study using a Hokuyo-based 3D rangefinder to model terrain with and without outstanding obstacles. Performance regarding error and model size are compared favorably with respect to a solution that uses quadric-based surface simplification (QSlim).

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Design and development of a fast and precise low-cost 3D laser rangefinder

Cited by 35 publications

References 10 publications

Navigability analysis of natural terrains with fuzzy elevation maps from ground-based 3D range scans

Navigability analysis of natural terrains with fuzzy elevation maps from ground-based 3D range scans

3D segmentation method for natural environments based on a Geometric-Featured Voxel map

Building Fuzzy Elevation Maps from a Ground-Based 3D Laser Scan for Outdoor Mobile Robots

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