ARAS: Ambiguity-aware Robust Active SLAM based on Multi-hypothesis State and Map Estimations

Hsiao, Ming; Mangelson, Joshua G.; Suresh, Sudharshan; Debrunner, C.; Kaess, Michael

doi:10.1109/iros45743.2020.9341384

Cited by 10 publications

(9 citation statements)

References 36 publications

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“…Pathak et al [240] incorporate, for the first time, reasoning about future data association hypotheses within a BSP framework, enabling autonomous hypotheses disambiguation. Another related work in this context is [241], that also reasons about ambiguous data association in future beliefs while utilizing the graphical model presented in [238]. A first-moment approximation to Bayesian inference with random sets of targets, known as the probability hypothesis density (PHD) filter, has been successfully applied to active target tracking problems [242].…”

Section: Robust Online Belief Space Planning and Active Slammentioning

confidence: 99%

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: Robust Online Belief Space Planning and Active Slammentioning

confidence: 99%

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

“…The authors of [20] incorporated, for the first time, reasoning about future data association hypotheses within a belief space planning framework, terming the corresponding approach DA-BSP. Another related work in this context is [8], that also reasons about ambiguous data association in future beliefs while utilizing the graphical model presented in [7]. To handle the exponential growth in the number of hypotheses, these approaches suggested to use different heuristics, e.g.…”

Section: Related Workmentioning

confidence: 99%

D2A-BSP: Distilled Data Association Belief Space Planning with Performance Guarantees Under Budget Constraints

Shienman¹,

Indelman²

2022

Preprint

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Unresolved data association in ambiguous and perceptually aliased environments leads to multi-modal hypotheses on both the robot's and the environment state. To avoid catastrophic results, when operating in such ambiguous environments, it is crucial to reason about data association within Belief Space Planning (BSP). However, explicitly considering all possible data associations, the number of hypotheses grows exponentially with the planning horizon and determining the optimal action sequence quickly becomes intractable. Moreover, with hard budget constraints where some non-negligible hypotheses must be pruned, achieving performance guarantees is crucial. In this work we present a computationally efficient novel approach that utilizes only a distilled subset of hypotheses to solve BSP problems while reasoning about data association. Furthermore, to provide performance guarantees, we derive error bounds with respect to the optimal solution. We then demonstrate our approach in an extremely aliased environment, where we manage to significantly reduce computation time without compromising on the quality of the solution.

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“…In [16] the authors introduced DA-BSP where, for the first time, reasoning about future data association hypotheses was incorporated within a BSP framework. The ARAS framework proposed in [7] leveraged the graphical model presented in [6] to reason about ambiguous data association in future beliefs. All of these approaches handled the exponential growth in the number of hypotheses by either pruning or merging.…”

Section: Related Workmentioning

confidence: 99%

“…To construct the belief tree in practice, we sample states from beliefs, sample data association given states and finally sample observations from (7).…”

Section: Constructing the Belief Tree Skeletonmentioning

confidence: 99%

See 1 more Smart Citation

Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints

Shienman¹,

Indelman²

2022

Preprint

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Autonomous agents operating in perceptually aliased environments should ideally be able to solve the data association problem. Yet, planning for future actions while considering this problem is not trivial. State of the art approaches therefore use multi-modal hypotheses to represent the states of the agent and of the environment. However, explicitly considering all possible data associations, the number of hypotheses grows exponentially with the planning horizon. As such, the corresponding Belief Space Planning problem quickly becomes unsolvable. Moreover, under hard computational budget constraints, some nonnegligible hypotheses must eventually be pruned in both planning and inference. Nevertheless, the two processes are generally treated separately and the effect of budget constraints in one process over the other was barely studied. We present a computationally efficient method to solve the nonmyopic Belief Space Planning problem while reasoning about data association. Moreover, we rigorously analyze the effects of budget constraints in both inference and planning.

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ARAS: Ambiguity-aware Robust Active SLAM based on Multi-hypothesis State and Map Estimations

Cited by 10 publications

References 36 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

D2A-BSP: Distilled Data Association Belief Space Planning with Performance Guarantees Under Budget Constraints

Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints

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