Fast 2D Map Matching Based on Area Graphs

Hou, Jiawei; Kuang, Haofei; Schwertfeger, Sören

doi:10.1109/robio49542.2019.8961732

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…The estimated transformation matrix was later checked for similarity, and a final alignment occurred. Similarly, in 2019, Hou et al [148] presented an area graph-based solution. In the method, they first segmented the grid map using the Area Graph.…”

Section: Alignment/matchingmentioning

confidence: 99%

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

Pokuciński,

Mrozek

2024

Applied Sciences

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The demand for digitizing manufacturing and controlling processes has been steadily increasing in recent years. Digitization relies on different techniques and equipment, which produces various data types and further influences the process of space understanding and area recognition. This paper provides an updated view of these data structures and high-level categories of techniques and methods leading to indoor environment segmentation and the discovery of its semantic meaning. To achieve this, we followed the Systematic Literature Review (SLR) methodology and covered a wide range of solutions, from floor plan understanding through 3D model reconstruction and scene recognition to indoor navigation. Based on the obtained SLR results, we identified three different taxonomies (the taxonomy of underlying data type, of performed analysis process, and of accomplished task), which constitute different perspectives we can adopt to study the existing works in the field of space understanding. Our investigations clearly show that the progress of works in this field is accelerating, leading to more sophisticated techniques that rely on multidimensional structures and complex representations, while the processing itself has become focused on artificial intelligence-based methods.

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Section: Alignment/matchingmentioning

confidence: 99%

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

Pokuciński,

Mrozek

2024

Applied Sciences

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“…Translation addendum: The work presented here has been used in the work on generating Area Graphs [20] and subsequently using those Area Graphs to match maps [21].…”

Section: Effect On Matchingmentioning

confidence: 99%

Room Detection for Topological Maps

Schwertfeger,

2019

Preprint

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Mapping is an important part of many robotic applications. In order to measure the performance of the mapping process we have to measure the quality of its result: the map. The map is essential for robotic algorithms like localization and path planning. Previously it was shown how matched Topology Graphs can be used for map evaluation by comparing the topology of the robot generated map to the topology of a ground truth map. In this paper we are extending the previous work by detecting open areas, for example rooms, in the 2D grid map and adding those to the topological representation. This way we can avoid the unreliable generation of paths in open areas, thus making the Topology Graph generation, and through that also the Topology Graph matching, more stable and robust. The detection applies the alpha shape algorithm for room detection.

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“…The segmentation on the furniture free map involves no over-segmentation of the rooms at all. We can also apply Area Graphs of furniture free maps in 2D map matching [22].…”

Section: Application To Map Segmentationmentioning

confidence: 99%

Furniture Free Mapping using 3D Lidars

He,

Hou,

Schwertfeger

2019

Preprint

Self Cite

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Mobile robots depend on maps for localization, planning, and other applications. In indoor scenarios, there is often lots of clutter present, such as chairs, tables, other furniture, or plants. While mapping this clutter is important for certain applications, for example navigation, maps that represent just the immobile parts of the environment, i.e. walls, are needed for other applications, like room segmentation or longterm localization. In literature, approaches can be found that use a complete point cloud to remove the furniture in the room and generate a furniture free map. In contrast, we propose a Simultaneous Localization And Mapping (SLAM)-based mobile laser scanning solution. The robot uses an orthogonal pair of Lidars. The horizontal scanner aims to estimate the robot position, whereas the vertical scanner generates the furniture free map. There are three steps in our method: point cloud rearrangement, wall plane detection and semantic labeling. In the experiment, we evaluate the efficiency of removing furniture in a typical indoor environment. We get 99.60% precision in keeping the wall in the 3D result, which shows that our algorithm can remove most of the furniture in the environment. Furthermore, we introduce the application of 2D furniture free mapping for room segmentation.

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Fast 2D Map Matching Based on Area Graphs

Cited by 7 publications

References 23 publications

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

Room Detection for Topological Maps

Furniture Free Mapping using 3D Lidars

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