Wireframe Mapping for Resource-Constrained Robots

Caccavale, Adam; Schwager, Mac

doi:10.1109/iros.2018.8594057

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…When required, the chosen map representation depends on the mission objective and environment. For example, in the case of ground robots in flat indoor environments, a 2D map might be sufficient (Caccavale and Schwager, 2018). In those scenarios, occupancy grid maps have been shown to be a compact and more accurate solution (Martin and Emami, 2010;Saeedi et al, 2011a) than feature-based maps (Benedettelli et al, 2010).…”

Section: Map Representationmentioning

confidence: 99%

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Lajoie

Ramtoula

Wu³

et al. 2022

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Motivated by the tremendous progress, we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area’s current trends and promising research avenues.

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Section: Map Representationmentioning

confidence: 99%

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Lajoie

Ramtoula

Wu³

et al. 2022

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show abstract

“…Voxels typically carry further information, such as occupancy probabilities [21,29] or a signed distance to the closest obstacle [17,22]. In two dimensions, an alternative way of representing known free space is to represent it with a polygon [5,12]. Here, the inside of the polygon represents free space, while its boundaries can either represent obstacles or the interface to unknown space.…”

Section: Map Representationsmentioning

confidence: 99%

Exploration Without Global Consistency Using Local Volume Consolidation

Cieslewski

Ziegler

Scaramuzza

2022

Springer Proceedings in Advanced Robotics

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“…Although, in indoor grid maps, straight line features are very common, it takes a long time to match only with straight lines, which cannot meet the requirements of high efficiency. In the research based on geometric features, Adam et al [26] proposed method based on wireframe mapping where the descriptor of each point is six variables and each edge is vectorized. Then, the weighted infinite norm between descriptors serves as a matching criterion.…”

Section: Initial Pose Unknown-feature Matchingmentioning

confidence: 99%

Map Merging with Suppositional Box for Multi-Robot Indoor Mapping

Chen

Zhao

et al. 2021

Electronics

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For the map building of unknown indoor environment, compared with single robot, multi-robot collaborative mapping has higher efficiency. Map merging is one of the fundamental problems in multi-robot collaborative mapping. However, in the process of grid map merging, image processing methods such as feature matching, as a basic method, are challenged by low feature matching rate. Driven by this challenge, a novel map merging method based on suppositional box that is constructed by right-angled points and vertical lines is proposed. The paper firstly extracts right-angled points of suppositional box selected from the vertical point which is the intersection of the vertical line. Secondly, based on the common edge characteristics between the right-angled points, suppositional box in the map is constructed. Then the transformation matrix is obtained according to the matching pair of suppositional boxes. Finally, for matching errors based on the length of pairs, Kalman filter is used to optimize the transformation matrix. Experimental results show that this method can effectively merge map in different scenes and the successful matching rate is greater than that of other features.

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Wireframe Mapping for Resource-Constrained Robots

Cited by 10 publications

References 18 publications

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Exploration Without Global Consistency Using Local Volume Consolidation

Map Merging with Suppositional Box for Multi-Robot Indoor Mapping

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