Incremental construction of the saturated-GVG for multi-hypothesis topological SLAM

Tang, Tao; Tully, Stephen; Kantor, George; Choset, Howie

doi:10.1109/icra.2011.5980524

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…Construction of Multi-layer Inputs 1) Data Representation on GVG: In this paper, our multilayer inputs is represented by the hierarchical generalized Voronoi graph (GVG) [4], a topological graph which has been successfully applied to navigation, localization and mapping. The general representation of GVG is composed of meet-points (locations of three-way or more equidistance to obstacles) and edges (feasible paths between meet-points which are two-way equidistance to obstacles) [28]. We adopt the same resolution as in our previous work [23] to construct the first layer GVG, and then higher layers of GVGs are constructed to describe the environment at different levels of granularity.…”

Section: Multi-layer Construction and Decision Makingmentioning

confidence: 99%

Place Classification With a Graph Regularized Deep Neural Network

Liao

Kodagoda

Wang

et al. 2017

IEEE Trans. Cogn. Dev. Syst.

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Place classification is a fundamental ability that a robot should possess to carry out effective human-robot interactions. It is a nontrivial classification problem which has attracted many research. In recent years, there is a high exploitation of Artificial Intelligent algorithms in robotics applications. Inspired by the recent successes of deep learning methods, we propose an end-to-end learning approach for the place classification problem. With the deep architectures, this methodology automatically discovers features and contributes in general to higher classification accuracies. The pipeline of our approach is composed of three parts. Firstly, we construct multiple layers of laser range data to represent the environment information in different levels of granularity. Secondly, each layer of data is fed into a deep neural network model for classification, where a graph regularization is imposed to the deep architecture for keeping local consistency between adjacent samples. Finally, the predicted labels obtained from all the layers are fused based on confidence trees to maximize the overall confidence. Experimental results validate the effectiveness of our end-to-end place classification framework in which both the multi-layer structure and the graph regularization promote the classification performance. Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information.

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Section: Multi-layer Construction and Decision Makingmentioning

confidence: 99%

Place Classification With a Graph Regularized Deep Neural Network

Liao

Kodagoda

Wang

et al. 2017

IEEE Trans. Cogn. Dev. Syst.

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“…117 In order to be able to recover from incorrect loop closures, Tully et al 112 introduced a multi-hypothesis approach based on a tree expansion algorithm specifically conceived for edge-ordered graphs, 32 as well as a series of pruning rules to keep the number of hypothesis under control. Recently, Tao et al 107 discussed the benefits of saturated generalized Voronoi graphs (S-GVG), that employ a wall-following behavior to navigate within sensor range limits, and performed SLAM using a similar hypothesis tree. Finally, Werner et al 119 suggested applying stochastic local search (SLS) to produce the topological map.…”

Section: Voronoi Graphs and Neighboring Information Choset And Nagatmentioning

confidence: 99%

Topological simultaneous localization and mapping: a survey

2013

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One of the main challenges in robotics is navigating autonomously through large, unknown, and unstructured environments. Simultaneous localization and mapping (SLAM) is currently regarded as a viable solution for this problem. As the traditional metric approach to SLAM is experiencing computational difficulties when exploring large areas, increasing attention is being paid to topological SLAM, which is bound to provide sufficiently accurate location estimates, while being significantly less computationally demanding. This paper intends to provide an introductory overview of the most prominent techniques that have been applied to topological SLAM in terms of feature detection, map matching, and map fusion.

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“…FastSLAM [11][12][13][14] is an efficient approach to SLAM based on particle filtering. Some other efficient algorithms have also been proposed, such as Rao-Blackwellized particle filters (RBPF), 15,16 Graph-SLAM, 17,18 Topological SLAM, [19][20][21] EM-based Incremental SLAM 22 and Prediction-based SLAM (P-SLAM). 23 However, most of the existing approaches assume that pre-placed artificial landmarks or sufficient natural features are available in the environment.…”

Section: Introductionmentioning

confidence: 99%

Active exploration using a scheme for autonomous allocation of landmarks

et al. 2013

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In this paper, we focus on the unknown environments without artificial landmarks and features, such as disaster situations and polar regions. An approach to active exploration based on an on-line scheme for autonomous allocation of landmarks is proposed. Specifically, the robot carries along with itself some landmarks which are to be allocated during the exploration according to some heuristic rules. The utility of landmark allocation is analyzed and calculated. Then the active exploration is converted into a problem of multi-objective optimization. The objective function includes three weighted terms: the accuracy of localization and mapping, the coverage rate of the unknown environment and the utility of the allocated landmarks. By solving this optimization problem, control inputs of the robot are computed to guarantee that accurate localization, high-quality mapping and complete exploration can be achieved simultaneously. Moreover, supplementation and redundancy elimination of the allocated landmarks are executed to make a complete and non-redundant coverage for the environment. Finally, some landmarks, together with a device for allocating these landmarks, are developed. Both experiment and simulation results are presented to demonstrate the effectiveness of the proposed approach.

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Incremental construction of the saturated-GVG for multi-hypothesis topological SLAM

Cited by 9 publications

References 13 publications

Place Classification With a Graph Regularized Deep Neural Network

Place Classification With a Graph Regularized Deep Neural Network

Topological simultaneous localization and mapping: a survey

Active exploration using a scheme for autonomous allocation of landmarks

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