Consistent observation grouping for generating metric-topological maps that improves robot localization

Blanco, Jose-Luis; González, J.; Fernández‐Madrigal, Juan‐Antonio

doi:10.1109/robot.2006.1641810

Cited by 33 publications

(42 citation statements)

References 12 publications

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“…[8,9] Because under the condition of a given robot path, the more the observations between submaps are closer to the conditional independence, the more this approximation of map segmentation can be ignored. [10,11] For this purpose, which is defined as (2), a map segmentation method based on spectral clustering is proposed.…”

Section: Analysis Of Map Segmentation Methods Used In Ruinsmentioning

confidence: 99%

Map segmentation for simultaneous localization and mapping in ruins

Wang

et al. 2016

Advanced Robotics

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The distribution of environmental features of the internal ruins which are formed by a randomly seismic disaster is unpredictable. Therefore, the existing methods of map segmentation, which need to preset parameters, cannot be directly used. Considering the lack of prior knowledge, a map segmentation method based on the spectral clustering is proposed in the framework of hierarchical simultaneous localization and mapping (SLAM) algorithm. The method solves the problem of incremental complexity of SLAM algorithm using the division of environment. In accordance with the similarity of observed environment, a weighted graph is established. The nodes in the graph are generated by measuring the expected information gain and position redundancy. Then, the graph is partitioned into subjective results of map segment based on the criterion of minimum normalized cut. On the basis of the inherent sparse of SLAM, the proposed algorithm not only reduces the cost of calculation, but also minimizes the loss of information in order to ensure the global consistency. Finally, the feasibility and effectiveness of the algorithm are verified by simulation and experiment. ARTICLE HISTORY

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Section: Analysis Of Map Segmentation Methods Used In Ruinsmentioning

confidence: 99%

Map segmentation for simultaneous localization and mapping in ruins

Wang

et al. 2016

Advanced Robotics

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“…There are two critical issues in this partitioning approach: first, the computation of the arc weights; and second, the criterion adopted to perform the partition itself. As for the first, the arc weights are assigned according to the Sensed-Space-Overlap (SSO), following our previous work [3], particularized for landmark observations. This simple but effective measure represents the information shared by two keyframes.…”

Section: Map Partitioning Methodsmentioning

confidence: 99%

Hybrid Metric-topological Mapping for Large Scale Monocular SLAM

Fernández-Moral

Arévalo

González-Jiménez

2014

Informatics in Control, Automation and Robotics

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Simultaneous Localization and Mapping (SLAM) is a central problem for autonomous mobile robotics. Monocular SLAM is one of the ways to tackle the problem, where the only input information are the images from a moving camera. Current approaches for this problem have achieved a good balance between accuracy and density of the map, however, they are not suited for large scale. In this paper, we present a dynamic mapping strategy where the metric map is divided into regions with highly connected observations, resulting in a topological structure which permits the efficient augmentation and optimization of the map. For that, a graph representation where the nodes represent keyframes, and their connections are a measure of their overlapping, is continuously rearranged. The experiments show that this hybrid metric-topological approach outperforms the efficiency and scalability of previous approaches.

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“…We augment G t−1 to reflect the robot's motion by adding a node n t to to the topology and an edge to the previous node n t−1 , resulting in an intermediate graph G 1) Creation of New Regions: We then probabilistically bisect the current region R c using the spectral clustering method outlined in Blanco et al [17]. We construct the similarity matrix using the laser point overlap between each pair of nodes in the region.…”

Section: A the Proposal Distributionmentioning

confidence: 99%

Learning spatial-semantic representations from natural language descriptions and scene classifications

Hemachandra

Walter

Tellex

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-We describe a semantic mapping algorithm that learns human-centric environment models from by interpreting natural language utterances. Underlying the approach is a coupled metric, topological, and semantic representation of the environment that enables the method to infer and fuse information from natural language descriptions with low-level metric and appearance data. We extend earlier work with a novel formulation incorporates spatial layout into a topological representation of the environment. We also describe a factor graph formulation of the semantic properties that encodes human-centric concepts such as type and colloquial name for each mapped region. The algorithm infers these properties by combining the user's natural language descriptions with image-and laser-based scene classification. We also propose a mechanism to more effectively ground natural language descriptions of spatially non-local regions using semantic cues from other modalities. We describe how the algorithm employs this learned semantic information to propose valid topological hypotheses, leading to more accurate topological and metric maps. We demonstrate that integrating language with other sensor data increases the accuracy of the achieved spatialsemantic representation of the environment.

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Consistent observation grouping for generating metric-topological maps that improves robot localization

Cited by 33 publications

References 12 publications

Map segmentation for simultaneous localization and mapping in ruins

Map segmentation for simultaneous localization and mapping in ruins

Hybrid Metric-topological Mapping for Large Scale Monocular SLAM

Learning spatial-semantic representations from natural language descriptions and scene classifications

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