Asynchronous and Parallel Distributed Pose Graph Optimization

Tian, Yulun; Koppel, Alec; Bedi, Amrit Singh; How, Jonathan P.

doi:10.1109/lra.2020.3010216

Cited by 31 publications

(26 citation statements)

References 18 publications

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“…Intuitively, this imbalance in the sizes of the sub-problems will produce an imbalance in the speed of solving each sub-problem and thus affects the utilization of computation power. In particular, for synchronous DPGO algorithms [8], the biggest sub-problem determines the global optimization speed, while for asynchronous DPGO algorithms [7], the speed of solving the imbalanced subproblems still affects the convergence properties, as will be verified in Sect.V.…”

Section: Introductionmentioning

confidence: 87%

“…In this method, the PGO problem is first transformed into a linear problem and then solved in a distributed manner using the Gauss-Seidel method [9]. Later the ASAPP method, based on distributed gradient descent, was proposed by Tian et al [7]. It can be seen as a distributed and asynchronous version of Riemann gradient descent [22].…”

Section: Related Workmentioning

confidence: 99%

“…Assume there are n robots, the distributed optimization problem for pose graph can then be written as [7], [8]…”

Section: B Distributed Pose Graph Optimizationmentioning

confidence: 99%

“…Recently, distributed pose graph optimization (DPGO) has become an increasingly popular research direction [6]- [8]. These methods adopt distributed optimization [6], [7], [9] to solve the PGO and have made considerable progress on addressing the scalability issue. The general idea of distributed optimization [9], [10] is to partition the global optimization problem into multiple sub-problems, set up the links between sub-problems.…”

Section: Introductionmentioning

confidence: 99%

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BDPGO: Balanced Distributed Pose Graph Optimization Framework for Swarm Robotics

Shen

2021

Preprint

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Distributed pose graph optimization (DPGO) is one of the fundamental techniques of swarm robotics. Currently, the sub-problems of DPGO are built on the native poses. Our validation proves that this approach may introduce an imbalance in the sizes of the sub-problems in real-world scenarios, which affects the speed of DPGO optimization, and potentially increases communication requirements. In addition, the coherence of the estimated poses is not guaranteed when the robots in the swarm fail, or partial robots are disconnected. In this paper, we propose BDPGO, a balanced distributed pose graph optimization framework using the idea of decoupling the robot poses and DPGO. BDPGO re-distributes the poses in the pose graph to the robot swarm in a balanced way by introducing a twostage graph partitioning method to build balanced subproblems. Our validation demonstrates that BDPGO significantly improves the optimization speed without changing the specific algorithm of DPGO in realistic datasets. What's more, we also validate that BDPGO is robust to robot failure, changes in the wireless network. BDPGO has capable of keeps the coherence of the estimated poses in these situations. The framework also has the potential to be applied to other collaborative simultaneous localization and mapping (CSLAM) problems involved in distributedly solving the factor graph.

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Section: Introductionmentioning

confidence: 87%

Section: Related Workmentioning

confidence: 99%

“…Assume there are n robots, the distributed optimization problem for pose graph can then be written as [7], [8]…”

Section: B Distributed Pose Graph Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

BDPGO: Balanced Distributed Pose Graph Optimization Framework for Swarm Robotics

Shen

2021

Preprint

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“…A smoother error function is exploited for the pose graph optimisation in [26]. The asynchronous property of distributed pose graph optimisation was investigated in [27] for multi-robot systems. All these methods work in SE(2), SE(3) and SIM (3), which are the common kinematics spaces in robotics.…”

Section: Related Workmentioning

confidence: 99%

Globally Optimal Fetoscopic Mosaicking Based on Pose Graph Optimisation With Affine Constraints

Bano

Deprest

et al. 2021

IEEE Robot. Autom. Lett.

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Fetoscopic laser ablation surgery could be guided using a high-quality panorama of the operating site, representing a map of the placental vasculature. This can be achieved during the initial inspection phase of the procedure using image mosaicking techniques. Due to the lack of camera calibration in the operating room, it has been mostly modelled as an affine registration problem. While previous work mostly focuses on image feature extraction for visual odometry, the challenges related to large-scale reconstruction (re-localisation, loop closure, drift correction) remain largely unaddressed in this context. This letter proposes using pose graph optimisation to produce globally optimal image mosaics of placental vessels. Our approach follows the SLAM framework with a front-end for visual odometry and a back-end for long-term refinement. Our front-end uses a recent state-of-the-art odometry approach based on vessel segmentation, which is then managed by a key-frame structure and the bagof-words (BoW) scheme to retrieve loop closures. The backend, which is our key contribution, models odometry and loop closure constraints as a pose graph with affine warpings between states. This problem in the special Euclidean space cannot be solved by existing pose graph algorithms and available libraries such as G2O. We model states on affine Lie group with local linearisation in its Lie algebra. The cost function is established using Mahalanobis distance with the vectorisation of the Lie algebra. Finally, an iterative optimisation algorithm is adopted to minimise the cost function. The proposed pose graph optimisation is first validated on simulation data with a synthetic trajectory that has different levels of noise and different numbers of loop closures. Then the whole system is validated using real fetoscopic data that has three sequences with different numbers of frames and loop closures. Experimental results validate the advantage of the proposed method compared with baselines.

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An introductory review of swarm technology for spacecraft on‐orbit servicing

Asri,

Zhu

2024

Int Journal of Mech Sys Dyn

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This review paper presents a comprehensive evaluation and forward‐looking perspective on the underexplored topic of servicing target objects using spacecraft swarms. Such targets can be known or unknown, cooperative or uncooperative, and pose significant challenges in modern space operations due to their inherent complexity and unpredictability. Successfully servicing space objects is vital for active debris removal and broader on‐orbit servicing tasks such as satellite maintenance, repair, refueling, orbital assembly, and construction. Significant effort has been invested in the literature to explore the servicing of targets using a single spacecraft. Given its advantages and benefits, this paper expands the discussion to encompass a swarm approach to the problem. This review covers various single‐spacecraft approaches and presents a critical examination of the existing, although limited, body of work dedicated to servicing orbital objects using multiple spacecraft. The focus is also broadened to include some influential studies concerning the characterization, capture, and manipulation of physical objects by general multiagent systems, a subject with significant parallels to the core interest of this manuscript. Furthermore, this article also delves into the realm of simultaneous localization and mapping, highlighting its application within close‐proximity operations in space, especially when dealing with unknown uncooperative targets. Special attention is paid to the benefits that this field can receive from distributed multiagent architectures. Finally, an exploration of the promising field of swarm robotics is presented, with an emphasis on its potential to revolutionize the servicing of orbital target objects. Concurrently, a survey of general research directly engaging swarms in the orbital context is conducted. This review aims to bridge the knowledge gap and stimulate further research in the underexplored domain of servicing space targets with spacecraft swarms.

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Asynchronous and Parallel Distributed Pose Graph Optimization

Cited by 31 publications

References 18 publications

BDPGO: Balanced Distributed Pose Graph Optimization Framework for Swarm Robotics

BDPGO: Balanced Distributed Pose Graph Optimization Framework for Swarm Robotics

Globally Optimal Fetoscopic Mosaicking Based on Pose Graph Optimisation With Affine Constraints

An introductory review of swarm technology for spacecraft on‐orbit servicing

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