Hierarchical topometric representation of 3D robotic maps

He, Zhenpeng; Sun, Haofei; Hou, Jiawei; Ha, Yajun; Schwertfeger, Sören

doi:10.1007/s10514-021-09991-8

Cited by 14 publications

(6 citation statements)

References 32 publications

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“…Nevertheless, we can further differentiate papers by the exact type of resulting model or the way of acquiring input data. Model generation from point cloud was presented by Ochmann et al [23], Shi et al [48], and He et al [63]. All the solutions generate 3D models aware of rooms and their connections described as doors or openings.…”

Section: D Model Reconstructionmentioning

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: D Model Reconstructionmentioning

confidence: 99%

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

Pokuciński,

Mrozek

2024

Applied Sciences

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“…The AreaGraph encodes the areas of rooms based on the architectural walls. We can create the AreaGraph from complete 3D point clouds [3] or from 2D grid maps [4], [5]. We obtain furniture-free 2D grid maps by either employing according to Simultaneous Localization and Mapping technologies [10], [11] or by rendering CAD building data into 2D grid maps.…”

Section: Arxiv:230904791v1 [Csro] 9 Sep 2023mentioning

confidence: 99%

“…Generating from a 3D pointcloud map. We follow our previous work [3] which generates hierarchical 3D topometric maps from 3D point clouds of the whole building, to extract 2D Area Graphs from a 3D pointcloud map.…”

Section: Automated Generationmentioning

confidence: 99%

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FloorplanNet: Learning Topometric Floorplan Matching for Robot Localization

Feng

Hou

et al. 2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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Maps are essential to mobile robotics tasks like localization and planning. We propose the open street map (osm) XML based Area Graph file format to store hierarchical, topometric semantic multi-floor maps of indoor and outdoor environments, since currently no such format is popular within the robotics community. Building on-top of osm we leverage the available open source editing tools and libraries of osm, while adding the needed mobile robotics aspect with building-level obstacle representation yet very compact, topometric data that facilitates planning algorithms. Through the use of common osm keys as well as custom ones we leverage the power of semantic annotation to enable various applications. For example, we support planning based on robot capabilities, to take the locomotion mode and attributes in conjunction with the environment information into account. The provided C++ library is integrated into ROS. We evaluate the performance of osmAG using real data in a global path planning application on a very big osmAG map, demonstrating its convenience and effectiveness for mobile robots.

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“…The work of [29] addresses a different, but related, problem. Starting from a 3D point-cloud map of the environment the method extracts a 2D occupancy map where different rooms are detected.…”

Section: Related Workmentioning

confidence: 99%

Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments from Occupancy Grid Maps

Luperto¹,

Kucner²,

Tassi³

et al. 2022

Preprint

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Identifying the environment's structure, i.e., to detect core components as rooms and walls, can facilitate several tasks fundamental for the successful operation of indoor autonomous mobile robots, including semantic environment understanding. These robots often rely on 2D occupancy maps for core tasks such as localisation, motion and task planning. However, reliable identification of structure and room segmentation from 2D occupancy maps is still an open problem due to clutter (e.g., furniture and movable object), occlusions, and partial coverage. We propose a method for the RObust StructurE identification and ROom SEgmentation (ROSE 2 ) of 2D occupancy maps, which may be cluttered and incomplete. ROSE 2 identifies the main directions of walls and is resilient to clutter and partial observations, allowing to extract a clean, abstract geometrical floor-plan-like description of the environment, which is used to segment, i.e., to identify rooms in, the original occupancy grid map. ROSE 2 is tested in several realworld publicly-available cluttered maps obtained in different conditions. The results show how it can robustly identify the environment structure in 2D occupancy maps suffering from clutter and partial observations, while significantly improving room segmentation accuracy. Thanks to the combination of clutter removal and robust room segmentation ROSE 2 consistently achieves higher performance than the state-of-the-art methods, against which it is compared.

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Hierarchical topometric representation of 3D robotic maps

Cited by 14 publications

References 32 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

FloorplanNet: Learning Topometric Floorplan Matching for Robot Localization

Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments from Occupancy Grid Maps

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