Accurate rough terrain estimation with space-carving kernels

Hadsell, Raia; Bagnell, J. Andrew; Huber, Daniel; Hebert, Martial

doi:10.15607/rss.2009.v.019

Cited by 38 publications

(20 citation statements)

References 23 publications

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“…In its most basic form, this results in an elevation map where the map stores exactly one value per cell (Hebert et al, 1989). One approach in which such maps have been demonstrated to be sufficient is the outdoor terrain navigation method described by Hadsell et al (2009). Whenever there is a single surface that the robot uses for navigation, an elevation map is sufficient to model the environment, since overhanging obstacles that are higher than the vehicle, such as trees, bridges or underpasses, can be safely ignored.…”

Section: Related Workmentioning

confidence: 99%

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

et al. 2013

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Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

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Section: Related Workmentioning

confidence: 99%

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

et al. 2013

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“…Terrain map estimation is an important component in outdoor mobile robot navigation, and several authors have addressed it ( [2][3][4][5][6][7][8][9][10][11][12][13]). Some of them have concentrated their effort on the fusion problem [12] while others have addressed the representation of the terrain data within the map [5] or the use of elevation functions over a 2D domain to represent the map [3].…”

Section: Related Workmentioning

confidence: 99%

“…Some of them have concentrated their effort on the fusion problem [12] while others have addressed the representation of the terrain data within the map [5] or the use of elevation functions over a 2D domain to represent the map [3]. Other authors have tackled the filtering of the laser range finder data to improve map quality [8], the assessment of the traversability of the terrain [9], the accurate registration of the laser range finder data [10][11][12][13] or the loop-closing problem when building a global map [5].…”

Section: Related Workmentioning

confidence: 99%

“…We address the specific problem of building coherent semi-local terrain maps with laser range finders whose 6D pose changes due to the irregular surface that the vehicle is traversing and the non-rigid characteristics of the vehicle (vehicle suspension system). We use the term semi-local map to denote a map that: (1) is built without using GPS information, (2) uses a global reference system that is defined at the beginning of the map estimation, (3) has an extension of several hundred meters, and (4) can be integrated onto the global map using the GPS and inertial data. We use semi-local maps because we are interested in achieving a very accurate 6D pose estimation of the laser range finders; the use of standard GPS (no differential) in this estimation process would produce inaccuracies that would be reflected in the generated map.…”

Section: Introductionmentioning

confidence: 99%

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A Kalman-filtering-based Approach for Improving Terrain Mapping in off-road Autonomous Vehicles

Parra‐Tsunekawa

Ruiz‐del‐Solar

Vallejos

2014

J Intell Robot Syst

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Artículo de publicación ISIThe generation of accurate terrain maps while navigating over off-road, irregular terrains is a complex challenge, due to the difficulties in the estimation of the pose of the laser rangefinders, which is required for the proper registration of the range measurements. This paper addresses this problem. The proposed methodology uses an Extended Kalman filter to estimate in real-time the instantaneous pose of the vehicle and the laser rangefinders by considering measurements acquired from an inertial measurement unit, internal sensorial data of the vehicle and the estimated heights of the four wheels, which are obtained from the terrain map and allow determination of the vehicle's inclination. The estimated 6D pose of the laser rangefinders is used to correctly project the laser measurements onto the terrain map. The terrain map is a 2.5D map that stores in each cell the mean value and variance of the terrain height. In each map's cell position, new laser observations are fused with existing height estimations using a Kalman filter. The proposed methodology is validated in the real world using an autonomous vehicle. Field trials show that the use of the proposed state estimation methodology produces maps with much higher accuracy than the standard approaches.FONDECYT 113015

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“…Digital elevation maps (DEMs) have been used to create a discrete geometric representation of the environment surfaces; however, some works have been investigated to create a more complete model using DEMs, i.e., to estimate elevation in regions where little or no data [1][2][3] are available. Nevertheless, these techniques have not yet been applied to build accurate land maps in uneven terrain.…”

Section: Introductionmentioning

confidence: 99%

Terrain Traversability and Optimal Path Planning in 3D Uneven Environment for an Autonomous Mobile Robot

et al. 2014

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A key issue in mobile robot applications involves building a map of the environment that may be used by the robot for path planning. In this paper, a new approach for uneven environment reconstruction based on the information supplied by a millimetre wave (MMW) radar has been proposed. The returned raw data from the MMW are stored in a database and then transferred to the CAD model, and represented as objects using an algorithm. The non-uniform rational B-splines have been used to extract the polygonal mesh decomposition equivalent to the 3D uneven environment and obstacle surfaces. The polygonal mesh decomposition helps to locate the different obstacles and to build the optimal path to reach the target starting from any initial position, by taking into consideration the terrain traversability. The optimal path is obtained by applying distance calculation method and speed limits. Indeed, the returned path is smooth which is a crucial performance consideration, especially for practical service robots.

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Accurate rough terrain estimation with space-carving kernels

Cited by 38 publications

References 23 publications

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

A Kalman-filtering-based Approach for Improving Terrain Mapping in off-road Autonomous Vehicles

Terrain Traversability and Optimal Path Planning in 3D Uneven Environment for an Autonomous Mobile Robot

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