A region-based SLAM algorithm capturing metric, topological, and semantic properties

Oberländer, Jan; Uhl, Klaus; Zöllner, J. Marius; Dillmann, Rüdiger

doi:10.1109/robot.2008.4543482

Cited by 19 publications

(15 citation statements)

References 13 publications

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“…In Oberländer et al (2008), the authors propose a FastSLAM based method to generate topological and semantic maps of the environment. Environment is represented as using a new type of feature, named regions.…”

Section: Hybrid Map Generating Approachesmentioning

confidence: 99%

Where am I? Creating spatial awareness in unmanned ground robots using SLAM: A survey

Dhiman

Deodhare

Khemani

2015

Sadhana

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This paper presents a survey of Simultaneous Localization And Mapping (SLAM) algorithms for unmanned ground robots. SLAM is the process of creating a map of the environment, sometimes unknown a priori, while at the same time localizing the robot in the same map. The map could be one of different types i.e. metrical, topological, hybrid or semantic. In this paper, the classification of algorithms is done in three classes: (i) Metric map generating approaches, (ii) Qualitative map generating approaches, and (iii) Hybrid map generating approaches. SLAM algorithms for both static and dynamic environments have been surveyed. The algorithms in each class are further divided based on the techniques used. The survey in this paper presents the current state-of-the-art methods, including important landmark works reported in the literature.

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Section: Hybrid Map Generating Approachesmentioning

confidence: 99%

Where am I? Creating spatial awareness in unmanned ground robots using SLAM: A survey

Dhiman

Deodhare

Khemani

2015

Sadhana

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“…However, it still requires matching numerous feature points in the images and also has an additional step of fitting higher-level structures in the tracked feature points. In [10], a region-based SLAM framework is presented that considers rectangular objects to create a map of the indoor environment. For outdoor environments the use of natural landmarks, such as trees, has been explored in [11].…”

Section: Related Workmentioning

confidence: 99%

Image moments for higher-level feature based navigation

Dani

Panahandeh

Chung

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper presents a novel vision-based localization and mapping algorithm using image moments of region features. The environment is represented using regions, such as planes and/or 3D objects instead of only a dense set of feature points. The regions can be uniquely defined using a small number of parameters; e.g., a plane can be completely characterized by normal vector and distance to a local coordinate frame attached to the plane. The variation of image moments of the regions in successive images can be related to the parameters of the regions. Instead of tracking a large number of feature points, variations of image moments of regions can be computed by tracking the segmented regions or a few feature points on the objects in successive images. A map represented by regions can be characterized using a minimal set of parameters. The problem is formulated as a nonlinear filtering problem. A new discrete-time nonlinear filter based on the state-dependent coefficient (SDC) form of nonlinear functions is presented. It is shown via Monte-Carlo simulations that the new nonlinear filter is more accurate and consistent than EKF by evaluating the root-mean squared error (RMSE) and normalized estimation error squared (NEES).

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“…This paper refers to simultaneous localization and map− ping (SLAM) problem in mobile robotics [11][12][13]. While the robot scans the environment using 3D cameras, subse− quent images should be aligned and merged to gain the detailed information about the surrounding objects.…”

Section: Related Workmentioning

confidence: 99%

On-line range images registration with GPGPU

Bedkowski

Naruniec

2013

Opto-Electronics Review

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This paper concerns implementation of algorithms in the two important aspects of modern 3D data processing: data registration and segmentation. Solution proposed for the first topic is based on the 3D space decomposition, while the latter on image processing and local neighbourhood search. Data processing is implemented by using NVIDIA compute unified device architecture (NIVIDIA CUDA) parallel computation. The result of the segmentation is a coloured map where different colours correspond to different objects, such as walls, floor and stairs. The research is related to the problem of collecting 3D data with a RGB-D camera mounted on a rotated head, to be used in mobile robot applications. Performance of the data registration algorithm is aimed for on-line processing. The iterative closest point (ICP) approach is chosen as a registration method. Computations are based on the parallel fast nearest neighbour search. This procedure decomposes 3D space into cubic buckets and, therefore, the time of the matching is deterministic. First technique of the data segmentation uses accele-rometers integrated with a RGB-D sensor to obtain rotation compensation and image processing method for defining pre-requisites of the known categories. The second technique uses the adapted nearest neighbour search procedure for obtaining normal vectors for each range point.

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A region-based SLAM algorithm capturing metric, topological, and semantic properties

Cited by 19 publications

References 13 publications

Where am I? Creating spatial awareness in unmanned ground robots using SLAM: A survey

Where am I? Creating spatial awareness in unmanned ground robots using SLAM: A survey

Image moments for higher-level feature based navigation

On-line range images registration with GPGPU

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