Alignment of Occupancy Grid and Floor Maps Using Graph Matching

Kakuma, Daisuke; Tsuichihara, Satoki; Ricardez, Gustavo Alfonso Garcia; Takamatsu, Jun; Ogasawara, Tsukasa

doi:10.1109/icsc.2017.38

Cited by 22 publications

(18 citation statements)

References 8 publications

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“…This section addresses the methods that establish the matches between the topological and metric maps. There are many methods that extract topological maps from occupancy grids (some examples are [99][100][101][102], but the outputs are very different.…”

Section: Metric Grid Vs Topological Map Mergingmentioning

confidence: 99%

Heterogeneous Map Merging: State of the Art

Andersone

2019

Robotics

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Multi-robot mapping and environment modeling have several advantages that makeit an attractive alternative to the mapping with a single robot: faster exploration, higherfault tolerance, richer data due to different sensors being used by different systems. However,the environment modeling with several robotic systems operating in the same area causes problemsof higher-order—acquired knowledge fusion and synchronization over time, revealing the sameenvironment properties using different sensors with different technical specifications. While theexisting robot map and environment model merging techniques allow merging certain homogeneousmaps, the possibility to use sensors of different physical nature and different mapping algorithms islimited. The resulting maps from robots with different specifications are heterogeneous, and eventhough some research on how to merge fundamentally different maps exists, it is limited to specificapplications. This research reviews the state of the art in homogeneous and heterogeneous mapmerging and illustrates the main research challenges in the area. Six factors are identified thatinfluence the outcome of map merging: (1) robotic platform hardware configurations, (2) maprepresentation types, (3) mapping algorithms, (4) shared information between robots, (5) relativepositioning information, (6) resulting global maps.

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Section: Metric Grid Vs Topological Map Mergingmentioning

confidence: 99%

Heterogeneous Map Merging: State of the Art

Andersone

2019

Robotics

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“…Few methods have been developed to match maps with a high level of abstraction. Instead, most works focus on matching partial metric maps or limit the extent to which maps can differ by using particular types of maps such as CAD models, aerial images, or blueprint layouts [4][5][6][7][8]. It is easy to understand why that is: similar maps can use easily describable similarity measures based on direct sensor measurements or some classic descriptors such as SIFT.…”

Section: Related Workmentioning

confidence: 99%

“…Kakuma et al [7] aligned an occupancy grid map with a floor map to obtain semantic information about the environment. They segmented the environment using morphological operations and created a graph out of the segmentation.…”

Section: Related Workmentioning

confidence: 99%

URSIM: Unique Regions for Sketch Map Interpretation and Matching

2019

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We present a method for matching sketch maps to a corresponding metric map, with the aim of later using the sketch as an intuitive interface for human-robot interactions. While sketch maps are not metrically accurate and many details, which are deemed unnecessary, are omitted, they represent the topology of the environment well and are typically accurate at key locations. Thus, for sketch map interpretation and matching, one cannot only rely on metric information. Our matching method first finds the most distinguishable, or unique, regions of two maps. The topology of the maps, the positions of the unique regions, and the size of all regions are used to build region descriptors. Finally, a sequential graph matching algorithm uses the region descriptors to find correspondences between regions of the sketch and metric maps. Our method obtained higher accuracy than both a state-of-the-art matching method for inaccurate map matching, and our previous work on the subject. The state of the art was unable to match sketch maps while our method performed only 10% worse than a human expert.

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“…A more common approach to the map alignment problem is to interpret the input maps with an abstract representation that enables a search on the similarity of instances. For example, graphs capture the canonical points of the open space as vertices, and the connectivity of the open space is represented by the edges between vertices (Huang and Beevers 2005;Schwertfeger and Birk 2013;Kakuma et al 2017). Consequently, geometric and/or topological similarities of the vertices and/or edges are used to find a match between two maps.…”

Section: Approachesmentioning

confidence: 99%

“…In order to benefit from semantic information available in floor maps for high level task execution, Kakuma et al (2017) proposed a graph matching based method for the alignment of sensor maps to floor plans of the buildings. Their method constructs a graph from segmented regions of the occupancy map.…”

Section: Graph Matching Approachesmentioning

confidence: 99%

2D map alignment with region decomposition

Shahbandi

Magnusson

2018

Auton Robot

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In many applications of autonomous mobile robots the following problem is encountered. Two maps of the same environment are available, one a prior map and the other a sensor map built by the robot. To benefit from all available information in both maps, the robot must find the correct alignment between the two maps. There exist many approaches to address this challenge, however, most of the previous methods rely on assumptions such as similar modalities of the maps, same scale, or existence of an initial guess for the alignment. In this work we propose a decomposition-based method for 2D spatial map alignment which does not rely on those assumptions. Our proposed method is validated and compared with other approaches, including generic data association approaches and map alignment algorithms. Real world examples of four different environments with thirty six sensor maps and four layout maps are used for this analysis. The maps, along with an implementation of the method, are made publicly available online.

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Alignment of Occupancy Grid and Floor Maps Using Graph Matching

Cited by 22 publications

References 8 publications

Heterogeneous Map Merging: State of the Art

Heterogeneous Map Merging: State of the Art

URSIM: Unique Regions for Sketch Map Interpretation and Matching

2D map alignment with region decomposition

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