Broadcast Your Weaknesses: Cooperative Active Pose-Graph SLAM for Multiple Robots

Zhao, Liang; Lee, Ki Myung Brian; Yoo, Chanyeol; Huang, Shoudong

doi:10.1109/lra.2020.2970665

Cited by 22 publications

(24 citation statements)

References 31 publications

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“…Application examples in the literature include voxel [55], [82] and semantic maps [98]. Stachniss et al [39] show that combining distance and information-based functions results in better exploration strategies, and this has since been a common approach, especially for multi-robot configurations [118]. However, manual tuning to overcome discrepancies between the multiple terms involved is needed [50], [119].…”

Section: A Naive Cost Functionsmentioning

confidence: 99%

“…Placed and Castellanos [74], [117] study the general active graph-SLAM problem formulated over the Lie group SE(n); showing the existing relationships between modern optimality criteria of the FIM and connectivity indices when the edges of the posegraph are weighted appropriately, and reporting substantial reductions in computation time. These results have been used in coverage problems [61], multi-robot exploration [118], or to develop a stopping criterion [150].…”

Section: The Graphical Structure Of the Problemmentioning

confidence: 99%

“…Best et al [220] propose the self-organizing map algorithm, considering the problem of multi-robot path planning for active perception and data collection tasks. Chen et al [118] leverage graph connectivity indices and their relationship to optimality criteria to achieve multi-robot active graph-SLAM. Each robot aims to improve the pose graphs of the other agents by sharing its observations when it moves near areas where they have low connectivity.…”

Section: Multi-robot Active Slammentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

Placed¹,

Strader²,

Carrillo³

et al. 2022

Preprint

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Active Simultaneous Localization and Mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this work, we survey the state-of-the-art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the classical solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and modern techniques based on deep reinforcement learning, and review related work on multi-robot coordination. The manuscript concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.

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Section: A Naive Cost Functionsmentioning

confidence: 99%

Section: The Graphical Structure Of the Problemmentioning

confidence: 99%

Section: Multi-robot Active Slammentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

Placed¹,

Strader²,

Carrillo³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To that end, (Dinnissen et al, 2012) uses reinforcement learning to determine the best moment to merge the local maps, and (Kontitsis et al, 2013) leverages instead the covariance matrix computed by the EKFbased inference engine to select trajectories that reduces the map uncertainty. Similarly, (Atanasov et al, 2015) develops a theoretical approach to design a sensor control policy which minimizes the entropy of the estimation task, while (Chen et al, 2020) proposes to broadcast the weakest nodes in the C-SLAM pose graph topology to actively increase the estimation accuracy.…”

Section: Active C-slammentioning

confidence: 99%

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

Lajoie,

Ramtoula,

et al. 2021

Preprint

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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 multirobot 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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“…information, create an occupancy grid map [9] using bundle adjustment (BA) [10] or a point cloud map using iterative closest point (ICP) [11]. As an important research direction, the SLAM problem can also combine with some other areas, such as path planning [12], [13] and control [14], called as active SLAM [15] and planning under uncertainty [16], to get more accurate results. This paper aims to solve the indoor navigation problem by obtaining the occupancy grid map and then performing the localization task in the obtained map.…”

Section: Introductionmentioning

confidence: 99%

Submap-Based Indoor Navigation System for the Fetch Robot

Leighton

Zhu

et al. 2020

IEEE Access

Self Cite

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In this paper, we present a novel navigation framework for the Fetch robot in a large-scale environment based on submapping techniques. This indoor navigation system is divided into a submap mapping part and an on-line localization part. For the mapping part, in order to deal with large environments or multi-story buildings, a submap mapping framework fusing two-dimensional (2D) laser scan and 3D point cloud from RGBD sensor is proposed using Google Cartographer. Meanwhile, several image datasets with corresponding poses are created from the RGBD sensor. Thanks to the submap framework, the error is limited corresponding to the size of the map, thus localization accuracy will be improved. For the on-line localization, so as to switch the submaps, the on-line images from the RGBD sensor are used to match the database images using DeepLCD, a deep learning based library for loop closure. Based on the information from DeepLCD and odometry, adaptive Monte Carlo localization (AMCL) is reinitialized to finish the localization task. In order to validate the result accuracy, reflectors and a motion capture system are used to compute the absolute trajectory error (ATE) and the relative pose error (RPE) based on the Gaussian-Newton (GN) algorithm. Finally, the proposed framework is tested on the Fetch simulator and the real Fetch robot, including both submap mapping and on-line localization.

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Broadcast Your Weaknesses: Cooperative Active Pose-Graph SLAM for Multiple Robots

Cited by 22 publications

References 31 publications

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

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

Submap-Based Indoor Navigation System for the Fetch Robot

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