Using 3d laser range data for slam in outdoor environments

Brenneke, C.; Wulf, Oliver; Wagner, Bernd

doi:10.1109/iros.2003.1250626

Cited by 62 publications

(43 citation statements)

References 9 publications

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“…indefinite shapes or patterns, although these techniques can be complex and computationally expensive [3]. The main advantage of using 3-D models of environments is that they are more descriptive and have richer information content than 2-D models in terms of the features extracted from the environments, resulting in less ambiguity in distinguishing features [4].…”

Section: -D Modeling Techniques Allow Describing Environments Includmentioning

confidence: 99%

“…The main advantage of using 3-D models of environments is that they are more descriptive and have richer information content than 2-D models in terms of the features extracted from the environments, resulting in less ambiguity in distinguishing features [4]. 3-D models are used in fields varying from robot motion planning and navigation [3,5,6,7], art and architecture [8,9,10,11,12] to industry/urban planning, water management, and forestry documentation [13,14,15]. 3-D models can be obtained using a variety of sensors measuring range or intensity.…”

Section: -D Modeling Techniques Allow Describing Environments Includmentioning

confidence: 99%

“…For instance, Brenneke et al in [3] proposed a technique for simultaneous localization and mapping (SLAM) in outdoor environments.…”

Section: -D Modeling Techniques Allow Describing Environments Includmentioning

confidence: 99%

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Novel Compression Algorithm Based on Sparse Sampling of 3-D Laser Range Scans

Dobrucali

Barshan

2012

The Computer Journal

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3-D models of environments can be very useful and are commonly employed in areas such as robotics, art and architecture, environmental planning and documentation. A 3-D model is typically comprised of a large number of measurements. When 3-D models of environments need to be transmitted or stored, they should be compressed efficiently to use the capacity of the communication channel or the storage medium effectively. In this thesis, we propose a novel compression technique based on compressive sampling, applied to sparse representations of 3-D laser range measurements. The main issue here is finding highly sparse representations of the range measurements, since they do not have such representations in common domains, such as the frequency domain. To solve this problem, we develop a new algorithm to generate sparse innovations between consecutive range measurements acquired while the sensor moves. We compare the sparsity of our innovations with others generated by estimation and filtering. Furthermore, we compare the compression performance of our lossy compression method with widely used lossless and lossy compression techniques.The proposed method offers small compression ratio and provides a reasonable compromise between reconstruction error and processing time.

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Section: -D Modeling Techniques Allow Describing Environments Includmentioning

confidence: 99%

Section: -D Modeling Techniques Allow Describing Environments Includmentioning

confidence: 99%

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Novel Compression Algorithm Based on Sparse Sampling of 3-D Laser Range Scans

Dobrucali

Barshan

2012

The Computer Journal

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“…Outdoor 3D maps have been addressed by the computer vision community for many years [12] [7]; and also more recently by the robotics community [17] [4]. The approach presented in [7] merges aerial images, airborne laser scans, and images and laser scans taken from the ground.…”

Section: Related Workmentioning

confidence: 99%

Localization and mapping in urban environments using mobile robots

Wolf¹,

Sukhatme²

2007

J. Braz. Comp. Soc.

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“…Outdoor 3D maps have been addressed by the computer vision community for many years [2] [3] and also more recently by the robotics community [4] [5]. There are diverse applications for 3D site modeling.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Terrain Mapping and Classification Using Hidden Markov Models

Wolf

Sukhatme

Fox

et al.

Proceedings of the 2005 IEEE International Conference on Robotics and Automation

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Abstract-This paper presents a new approach for terrain mapping and classification using mobile robots with 2D laser range finders. Our algorithm generates 3D terrain maps and classifies navigable and non-navigable regions on those maps using Hidden Markov models. The maps generated by our approach can be used for path planning, navigation, local obstacle avoidance, detection of changes in the terrain, and object recognition. We propose a map segmentation algorithm based on Markov Random Fields, which removes small errors in the classification. In order to validate our algorithms, we present experimental results using two robotic platforms. I. INTRODUCTIONAutonomous navigation is a fundamental capability for a mobile robot. When traversing rough terrain, the robot must have the ability to avoid not only obstacles but also parts of the terrain that are considered not safe for navigation [1]. In this paper, we present an online algorithm that builds a 3D map of the terrain and classifies the mapped regions as navigable or non-navigable areas. The maps created by our approach have numerous applications such as: local avoidance of non-navigable areas, path planning, and object matching and recognition. We are particularly interested in the first two applications.Outdoor 3D maps have been addressed by the computer vision community for many years Different methods have been used to create the 3D representations of the environment such as: point clouds [7], triangular meshes, and planar structures [10]. Our approach uses point clouds to represent the terrain. This method can represent fine details of the environment and it is also easy and fast to compute. On the other hand, it requires considerable memory space to represent large areas.Part of the 3D outdoor mapping effort by the robotics community is focused on the 3D terrain mapping problem. This is an important problem when one is exploring unknown terrain. Applications for terrain mapping range from path planning and local obstacle avoidance to detection of changes

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Using 3d laser range data for slam in outdoor environments

Cited by 62 publications

References 9 publications

Novel Compression Algorithm Based on Sparse Sampling of 3-D Laser Range Scans

Novel Compression Algorithm Based on Sparse Sampling of 3-D Laser Range Scans

Localization and mapping in urban environments using mobile robots

Autonomous Terrain Mapping and Classification Using Hidden Markov Models

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