A system for volumetric robotic mapping of abandoned mines

Thrun, Sebastian; Hähnel, Dirk; Ferguson, Dave; Montemerlo, Michael; Triebel, Rudolph; Burgard, Wolfram; Baker, Christopher; Omohundro, Zachary; Thayer, Scott; Whittaker, William

doi:10.1109/robot.2003.1242260

Cited by 204 publications

(132 citation statements)

References 15 publications

(22 reference statements)

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“…The data set used here was collected by the subterranean robotics team on May 14, 2007, and comprises a 1,300-m traverse through the mine, including several loops ( Figure 13). This site has also been described by Thrun et al (2003), but it is important to note that although most prior data sets consisted of stationary laser scans, the data used here were collected while the robotic vehicle was continuously moving.…”

Section: Map Goodness: Coal Minementioning

confidence: 99%

Segmented SLAM in three‐dimensional environments

Fairfield¹,

Wettergreen²,

Kantor³

2009

Journal of Field Robotics

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Simultaneous localization and mapping (SLAM) has been shown to be feasible in many small, two-dimensional, structured domains. The next challenge is to develop real-time SLAM methods that enable robots to explore large, three-dimensional, unstructured environments and allow subsequent operation in these environments over long periods of time. To circumvent the scale limitations inherent in SLAM, the world can be divided up into more manageable pieces. SLAM can be formulated on these pieces by using a combination of metric submaps and a topological map of the relationships between submaps. The contribution of this paper is a realtime, three-dimensional SLAM approach that combines an evidence grid-based volumetric submap representation, a robust Rao-Blackwellized particle filter, and a topologically flexible submap segmentation framework and map representation. We present heuristic methods for deciding how to segment the world and for reconstructing large-scale metric maps for the purpose of closing loops. We demonstrate our method on a mobile robotic platform operating in large, three-dimensional environments. C 2009 Wiley Periodicals, Inc.

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Section: Map Goodness: Coal Minementioning

confidence: 99%

Segmented SLAM in three‐dimensional environments

Fairfield¹,

Wettergreen²,

Kantor³

2009

Journal of Field Robotics

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“…These improvements can be used to obtain and process a better robot environment information in the field of mobile robotics [1], [2], [3]. By now, methods for achieving tasks such as localization [4], [5], [6], navigation [7], [8] or automatic map building [9], were restricted to the two dimensional world which could be captured by the robot sensors. Nevertheless, using the new 3D sensors such as stereo cameras or 3D laser range finders it is possible to improve the representation of observed objects in order to use them into applications such as augmented reality, architecture, manufacturing process, SLAM problem, etc.…”

Section: Introductionmentioning

confidence: 99%

Large scale egomotion and error analysis with visual features

Quevedo¹,

Hernando²,

Gutiérrez³

et al. 2010

jopha

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Abstract-Several works deal with 3D data in SLAM problem but many of them are focused on short scale maps. In this paper, we propose a method that can be used for computing the 6DoF trajectory performed by a robot from the stereo images captured during a large scale trajectory. The method transforms robust 2D features extracted from the reference stereo images to the 3D space. These 3D features are then used for obtaining the correct robot movement. Both Sift and Surf methods for feature extraction have been used. Also, a comparison between our method and the results of the ICP algorithm have been performed. We have also made a study about errors in stereo cameras.

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“…For these and other reasons, modeling buildings in 3D has been a long-standing goal of researchers in computer vision [4], [5], [6], [7]. Generating such maps with robots would make it possible to acquire maps of environments inaccessible to people [8], [9], such as abandoned mines that have recently been mapped in 3D by mobile robots [10].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Expectation-Maximization Algorithm for Acquiring Multiplanar Maps of Indoor Environments With Mobile Robots

Thrun

Märtin

Liu

et al. 2004

IEEE Trans. Robot. Automat.

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115

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Abstract-This paper presents a real-time algorithm for acquiring compact 3D maps of indoor environments, using a mobile robot equipped with range and imaging sensors. Building on previous work on real-time pose estimation during mapping [1], our approach extends the popular expectation maximization algorithm [2] to multi-surface models, and makes it amenable to real-time execution. Maps acquired by our algorithm consist of compact sets of textured polygons that can be visualized interactively. Experimental results obtained in corridor-type environments illustrate that compact and accurate maps can be acquired in real-time and in a fully automated fashion.

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A system for volumetric robotic mapping of abandoned mines

Cited by 204 publications

References 15 publications

Segmented SLAM in three‐dimensional environments

Segmented SLAM in three‐dimensional environments

Large scale egomotion and error analysis with visual features

A Real-Time Expectation-Maximization Algorithm for Acquiring Multiplanar Maps of Indoor Environments With Mobile Robots

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