ExplORB-SLAM: Active Visual SLAM Exploiting the Pose-graph Topology

Placed, Julio A.; Rodriguez, Juan; Tardós, Juan D.; Castellanos, José A.

doi:10.1007/978-3-031-21065-5_17

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…Similar to information-entropy-based methods, the Theory of Optimal Experimental Design (TOED) can also be used to account for the utility of performing the active mapping action, and each action is considered as a stochastic design, while comparisons among designs are made using their associated covariance matrices via the optimality criteria, including A-opt, D-opt, and E-opt. The work in [19,20] discussed the general relationship between optimality criteria and connectivity indices when using TOED for active Graph-SLAM.…”

Section: Information-entropy-based Methodsmentioning

confidence: 99%

VP-SOM: View-Planning Method for Indoor Active Sparse Object Mapping Based on Information Abundance and Observation Continuity

Zhang,

Wang

2023

Sensors

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Active mapping is an important technique for mobile robots to autonomously explore and recognize indoor environments. View planning, as the core of active mapping, determines the quality of the map and the efficiency of exploration. However, most current view-planning methods focus on low-level geometric information like point clouds and neglect the indoor objects that are important for human–robot interaction. We propose a novel View-Planning method for indoor active Sparse Object Mapping (VP-SOM). VP-SOM takes into account for the first time the properties of object clusters in the coexisting human–robot environment. We categorized the views into global views and local views based on the object cluster, to balance the efficiency of exploration and the mapping accuracy. We developed a new view-evaluation function based on objects’ information abundance and observation continuity, to select the Next-Best View (NBV). Especially for calculating the uncertainty of the sparse object model, we built the object surface occupancy probability map. Our experimental results demonstrated that our view-planning method can explore the indoor environments and build object maps more accurately, efficiently, and robustly.

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Section: Information-entropy-based Methodsmentioning

confidence: 99%

VP-SOM: View-Planning Method for Indoor Active Sparse Object Mapping Based on Information Abundance and Observation Continuity

Zhang,

Wang

2023

Sensors

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“…This environment did not involve any obstacle avoidance tasks; therefore, there was no need to employ a SLAM method. Juna et al presented an open-source Active SLAM framework that leverages the Graph SLAM system [28]. The framework uses fast utility computation by exploiting and implementing path planning through DQN.…”

Section: Of 19mentioning

confidence: 99%

“…Table 1 summarizes the differences between our approach and other mentioned methods. [21] EKF SLAM A* -√ -Placed et al [22] Open Karto Dijkstra -√ -Suresh et al [23] Graph SLAM RRT 2 -√ -Chen et al [27] -DQN 3 ,GNN 4 T-opt √ -Juna et al [28] Graph SLAM DQN D-opt √ -Castellanos et al [29] Gmapping D3QN √ √…”

Section: Of 19mentioning

confidence: 99%

Exploration- and Exploitation-Driven Deep Deterministic Policy Gradient for Active SLAM in Unknown Indoor Environments

Zhao,

Hwang

2024

Electronics

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This study proposes a solution for Active Simultaneous Localization and Mapping (Active SLAM) of robots in unknown indoor environments using a combination of Deep Deterministic Policy Gradient (DDPG) path planning and the Cartographer algorithm. To enhance the convergence speed of the DDPG network and minimize collisions with obstacles, we devised a unique reward function that integrates exploration and exploitation strategies. The exploration strategy allows the robot to achieve the shortest running time and movement trajectory, enabling efficient traversal of unmapped environments. Moreover, the exploitation strategy introduces active closed loops to enhance map accuracy. We conducted experiments using the simulation platform Gazebo to validate our proposed model. The experimental results demonstrate that our model surpasses other Active SLAM methods in exploring and mapping unknown environments, achieving significant grid completeness of 98.7%.

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“…Placed and Castellanos [81], [160] 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 [62], multi-robot exploration [128], active visual SLAM [66], or to develop a stopping criterion [161].…”

Section: The Graphical Structure Of the Problemmentioning

confidence: 99%

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

Placed

Strader

Carrillo³

et al. 2023

IEEE Trans. Robot.

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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-ofthe-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 well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including beliefspace planning and deep reinforcement learning techniques, 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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ExplORB-SLAM: Active Visual SLAM Exploiting the Pose-graph Topology

Cited by 7 publications

References 14 publications

VP-SOM: View-Planning Method for Indoor Active Sparse Object Mapping Based on Information Abundance and Observation Continuity

VP-SOM: View-Planning Method for Indoor Active Sparse Object Mapping Based on Information Abundance and Observation Continuity

Exploration- and Exploitation-Driven Deep Deterministic Policy Gradient for Active SLAM in Unknown Indoor Environments

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

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