Rough Terrain Reconstruction for Rover Motion Planning

Gingras, David R.; Lamarche, Tom; Bedwani, Jean-Luc; Dupuis, Érick

doi:10.1109/crv.2010.32

Cited by 32 publications

(28 citation statements)

References 23 publications

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“…Three-dimensional (3D) point clouds provide valuable information in mobile robotics applications such as planetary exploration [1] [2] [3] [4], urban search and rescue [5] [6], and navigation on natural terrain [7]. However, as point cloud maps require coping with a huge amount of spatial information [8] [9], a simplified and compact representation of navigable terrain is necessary for motion planning [1] or tele-operation [10].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

“…However, as point cloud maps require coping with a huge amount of spatial information [8] [9], a simplified and compact representation of navigable terrain is necessary for motion planning [1] or tele-operation [10].…”

Section: Introductionmentioning

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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“…QSlim has been employed for terrain simplification of topographical maps [31,32]. However, QSlim cannot be directly applied to the Delaunay mesh corresponding to raw ground-based scans because of artifacts due to shadows and salient objects [8]. Therefore, preprocessing is necessary to produce a proper surface to start with.…”

Section: Comparison With Qslimmentioning

confidence: 99%

“…In this sense, three-dimensional (3D) laser scans provide valuable information for applications such as planetary exploration [3][4][5] or urban search and rescue [6,7]. However, as point clouds require coping with a huge amount of spatial data, a simplified and compact representation of navigable terrain is necessary for both motion planning [8] and tele-operation [9].…”

Section: Introductionmentioning

confidence: 99%

“…In robotics, elevation has been generally represented by regular grids [10][11][12] or by irregular triangular meshes [4,13]. Removal of artifacts and mesh simplification algorithms, like mesh decimation [14] and quadric-based polygonal surface simplification (QSlim) vertex clustering [15], can improve the compactness and reliability of these maps [8]. Nevertheless, as tesselated models have limitations in the face of incomplete and uncertain sensor data, some works have proposed using tools that can model elevation in a more robust and natural way.…”

Section: Introductionmentioning

confidence: 99%

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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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