Active SLAM and Exploration with Particle Filters Using Kullback-Leibler Divergence

Carlone, Luca; Du, Juan; Ng, Miguel Kaouk; Bona, Basilio; Indri, Marina

doi:10.1007/s10846-013-9981-9

Cited by 86 publications

(67 citation statements)

References 20 publications

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“…A popular framework for active SLAM consists of selecting the best future action among a finite set of alternatives. This family of active SLAM algorithms proceeds in three main steps [15,35]: 1) The robot identifies possible locations to explore or exploit, i.e. vantage locations, in its current estimate of the map; 2) The robot computes the utility of visiting each vantage point and selects the action with the highest utility; and 3) The robot carries out the selected action and decides if it is necessary to continue or to terminate the task.…”

Section: Active Slammentioning

confidence: 99%

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Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Cadena¹,

Carlone²,

Carrillo³

et al. 2016

IEEE Trans. Robot.

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3,112

1,742

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Abstract-Simultaneous localization and mapping (SLAM) consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications and witnessing a steady transition of this technology to industry. We survey the current state of SLAM and consider future directions. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

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Section: Active Slammentioning

confidence: 99%

“…If such posterior were known, an information-theoretic function, as the information gain, could be used to rank the different actions [22,233]. However, computing this joint probability analytically is, in general, computationally intractable [35,76,233]. In practice, one resorts to approximations.…”

Section: Active Slammentioning

confidence: 99%

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Cadena¹,

Carlone²,

Carrillo³

et al. 2016

IEEE Trans. Robot.

Self Cite

3,112

1,742

View full text Add to dashboard Cite

show abstract

“…There are several works that perform active SLAM with sensors such as lasers for 2D/3D mapping [27], [28], [29], but Davison and Murray were the first who integrated motion with stereo visual SLAM [30] where their objective was to minimize the trajectory error. Most active SLAM algorithms are based on maximizing mutual information [8], [31], which is also referred to as maximizing information gain [32], [33].…”

Section: Active Slam With Information Divergencementioning

confidence: 99%

Application-oriented design space exploration for SLAM algorithms

Saeedi

Nardi

Johns

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-In visual SLAM, there are many software and hardware parameters, such as algorithmic thresholds and GPU frequency, that need to be tuned; however, this tuning should also take into account the structure and motion of the camera. In this paper, we determine the complexity of the structure and motion with a few parameters calculated using information theory. Depending on this complexity and the desired performance metrics, suitable parameters are explored and determined.Additionally, based on the proposed structure and motion parameters, several applications are presented, including a novel active SLAM approach which guides the camera in such a way that the SLAM algorithm achieves the desired performance metrics. Real-world and simulated experimental results demonstrate the effectiveness of the proposed design space and its applications.

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“…Recent works in this area are focused on designing an on-line algorithm that estimates current position and previous results and sets goal point in order to maximize the map coverage and accuracy [5]- [7]. Some of recently proposed techniques include methods based on Kullback-Leibler divergence for evaluating the SLAM posterior approximations [7]; hierarchical Bayesian approach to determine what area should be visited next [6]; model predictive control and using an attractor to incorporate long term goals [8]; actively closing loops that are made during the exploration [5].…”

Section: Introductionmentioning

confidence: 99%

“…Some of recently proposed techniques include methods based on Kullback-Leibler divergence for evaluating the SLAM posterior approximations [7]; hierarchical Bayesian approach to determine what area should be visited next [6]; model predictive control and using an attractor to incorporate long term goals [8]; actively closing loops that are made during the exploration [5].…”

Section: Introductionmentioning

confidence: 99%

Active SLAM-based algorithm for autonomous exploration with mobile robot

Trivun

Salaka

Osmanković

et al. 2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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In this paper, we present an algorithm for fully autonomous exploration and mapping of an unknown indoor robot environment. This algorithm is based on the active SLAM (simultaneous localization and mapping) approach. The mobile robot equipped with laser sensor builds a map of an environment, while keeping track of its current location. Autonomy is introduced to this system by automatically setting goal points so that either previously unknown space is mapped, or known landmarks are revisited in order to increase map accuracy. Final aim is to maximize both map coverage and accuracy. The proposed procedure is experimentally verified on Pioneer 3-DX mobile robot in real environment, using ROS framework for implementation.

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Active SLAM and Exploration with Particle Filters Using Kullback-Leibler Divergence

Cited by 86 publications

References 20 publications

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Application-oriented design space exploration for SLAM algorithms

Active SLAM-based algorithm for autonomous exploration with mobile robot

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