Predicting the Layout of Partially Observed Rooms from Grid Maps

Luperto, Matteo; Arcerito, Valerio; Amigoni, Francesco

doi:10.1109/icra.2019.8793489

Cited by 18 publications

(29 citation statements)

References 18 publications

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“…The experimental evaluation is performed by showing how our method effectively identifies the structure and performs room segmentation in several environments, both using real-world cluttered and partial occupancy maps and on the benchmark dataset for room segmentation of [10]. Thanks to the combination of clutter removal and robust room segmentation ROSE 2 consistently achieves a higher performance when compared with the three methods from the state-of-the-art discussed in [10].ß This paper builds on previously published work about different yet related topics, on structure extraction [14], [15] and shape prediction of unobserved rooms [16]. In this paper, we integrate an improved version of our work of [16] with the robust feature extraction method of [14] to achieve a framework that could be robustly used on all types of 2D occupancy maps for structure extraction and room segmentation.…”

Section: Introductionmentioning

confidence: 69%

“…Thanks to the combination of clutter removal and robust room segmentation ROSE 2 consistently achieves a higher performance when compared with the three methods from the state-of-the-art discussed in [10].ß This paper builds on previously published work about different yet related topics, on structure extraction [14], [15] and shape prediction of unobserved rooms [16]. In this paper, we integrate an improved version of our work of [16] with the robust feature extraction method of [14] to achieve a framework that could be robustly used on all types of 2D occupancy maps for structure extraction and room segmentation. This improves the overall performance of the framework in two ways.…”

Section: Introductionmentioning

confidence: 79%

“…First, it simplifies the steps of the method and removes the requirement of a manual tuning of a set of parameters for each type of maps (as partial or complete maps, simulated or real ones). Second, we remove the limitation of [16] to work only with clean simulated maps while also significantly improving performance in detecting the environment's structure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Introductionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

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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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“…Finally, the use of partial prior knowledge and of combination of different types of prior knowledge could be studied, using an approach similar to that of [33].…”

Section: Discussionmentioning

confidence: 99%

Robot exploration of indoor environments using incomplete and inaccurate prior knowledge

Luperto

Antonazzi

Amigoni

et al. 2020

Robotics and Autonomous Systems

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Exploration is a task in which autonomous mobile robots incrementally discover features of interest in initially unknown environments. We consider the problem of exploration for map building, in which a robot explores an indoor environment in order to build a metric map. Most of the current exploration strategies used to select the next best locations to visit ignore prior knowledge about the environments to explore that, in some practical cases, could be available. In this paper, we present an exploration strategy that evaluates the amount of new ar

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“…There are also methods to complement the unobserved parts of the environment map using the information of the layout of the rooms [19][20][21]. In particular, Luperto et al proposed a method that identifies the layout of a partially known room from the walls on the 2D grid map and estimates the room layout from the known parts of the environment to the unknown parts by propagating the regularities [21]. However, map completion functions as post-processing of SLAM and is limited to partial area completion.…”

Section: Simultaneous Localization and Mapping (Slam)mentioning

confidence: 99%

Map completion from partial observation using the global structure of multiple environmental maps

Katsumata,

Kanechika,

Taniguchi

et al. 2021

Preprint

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Using the spatial structure of various indoor environments as prior knowledge, the robot would construct the map more efficiently. Autonomous mobile robots generally apply simultaneous localization and mapping (SLAM) methods to understand the reachable area in newly visited environments. However, conventional mapping approaches are limited by only considering sensor observation and control signals to estimate the current environment map. This paper proposes a novel SLAM method, map completion network-based SLAM (MCN-SLAM), based on a probabilistic generative model incorporating deep neural networks for map completion. These map completion networks are primarily trained in the framework of generative adversarial networks (GANs) to extract the global structure of large amounts of existing map data. We show in experiments that the proposed method can estimate the environment map 1.3 times better than the previous SLAM methods in the situation of partial observation.

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Predicting the Layout of Partially Observed Rooms from Grid Maps

Cited by 18 publications

References 18 publications

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

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

Robot exploration of indoor environments using incomplete and inaccurate prior knowledge

Map completion from partial observation using the global structure of multiple environmental maps

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