Robot-Assisted Surveillance in Large Environments

Capezio, Francesco; Mastrogiovanni, Fulvio; Sgorbissa, Antonio; Zaccaria, Renato

doi:10.2498/cit.1001180

Cited by 8 publications

(5 citation statements)

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“…With a growing demand for autonomous robots in a range of applications, such as search and rescue [42,8], and space and underwater exploration [7], it is essential for the robots to be able to navigate accurately for an extended period of time in order to accomplish the assigned tasks. To this end, the ability to detect revisits (i.e., loop closure or place recognition) becomes necessary, since it allows the robots to bound the errors and uncertainty in the estimates of their positions and orientations (poses).…”

Section: Introductionmentioning

confidence: 99%

Sparse optimization for robust and efficient loop closing

Latif

Huang

Leonard

et al. 2017

Robotics and Autonomous Systems

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It is essential for a robot to be able to detect revisits or loop closures for long-term visual navigation.A key insight explored in this work is that the loop-closing event inherently occurs sparsely, i.e., the image currently being taken matches with only a small subset (if any) of previous images. Based on this observation, we formulate the problem of loop-closure detection as a sparse, convex 1 -minimization problem. By leveraging fast convex optimization techniques, we are able to efficiently find loop closures, thus enabling real-time robot navigation. This novel formulation requires no offline dictionary learning, as required by most existing approaches, and thus allows online incremental operation. Our approach ensures a unique hypothesis by choosing only a single globally optimal match when making a loop-closure decision. Furthermore, the proposed formulation enjoys a flexible representation with no restriction imposed on how images should be represented, while requiring only that the representations are "close" to each other when the corresponding images are visually similar. The proposed algorithm is validated extensively using real-world datasets. IntroductionWith a growing demand for autonomous robots in a range of applications, such as search and rescue [42,8], and space and underwater exploration [7], it is essential for the robots to be able to navigate accurately for an extended period of time in order to accomplish the assigned tasks. To this end, the ability to detect revisits (i.e., loop closure or place recognition) becomes necessary, since it allows the robots to bound the errors and uncertainty in the estimates of their positions and orientations (poses). In this work, we particularly focus on 1 Corresponding author, loop closure during visual navigation, i.e., given a camera stream we aim to efficiently determine whether the robot has previously seen the current place or not.Even though the problem of loop closure has been extensively studied in the visual-SLAM literature (e.g., see [30,13,21]), a vast majority of existing algorithms typically require the offline training of visual words (dictionary) from a priori images that are acquired previously in visually similar environments. Clearly, this is not always the case when a robot operates in an unknown, drastically different environment. In general, it is difficult to reliably find loops in (visual) appearance space. One particular challenge is the perceptual aliasing -that is, while images may be similar in appearance, they might be coming from different places.To mitigate this issue, both temporal (i.e., loops will only be considered closed if there are other loops closed nearby) and geometric constraints (i.e., if a loop has to be considered closed, a valid transformation must exist between the matched images) can be employed [21]. It is important to point out that the approach of [21] decides on the quality of a match locally -If the match with the highest score (in some distance measure) is away from the second highest, it is consid...

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Section: Introductionmentioning

confidence: 99%

Sparse optimization for robust and efficient loop closing

Latif

Huang

Leonard

et al. 2017

Robotics and Autonomous Systems

View full text Add to dashboard Cite

show abstract

“…With a growing demand for autonomous robots in a range of applications, such as search and rescue [30,8], space and underwater exploration [7], it is essential for the robots to be able to navigate accurately for an extended period of time in order to accomplish the assigned tasks. To this end, the ability to detect revisits (i.e., loop closure or place recognition) becomes necessary, since it allows the robots to bound the errors and uncertainty in the estimates of their positions and orientations (poses).…”

Section: Introductionmentioning

confidence: 99%

An Online Sparsity-Cognizant Loop-Closure Algorithm for Visual Navigation

Latif

Huang

Leonard

et al. 2014

Robotics: Science and Systems X

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Abstract-It is essential for a robot to be able to detect revisits or loop closures for long-term visual navigation. A key insight is that the loop-closing event inherently occurs sparsely, i.e., the image currently being taken matches with only a small subset (if any) of previous observations. Based on this observation, we formulate the problem of loop-closure detection as a sparse, convex 1-minimization problem. By leveraging on fast convex optimization techniques, we are able to efficiently find loop closures, thus enabling real-time robot navigation. This novel formulation requires no offline dictionary learning, as required by most existing approaches, and thus allows online incremental operation. Our approach ensures a global, unique hypothesis by choosing only a single globally optimal match when making a loop-closure decision. Furthermore, the proposed formulation enjoys a flexible representation, with no restriction imposed on how images should be represented, while requiring only that the representations be close to each other when the corresponding images are visually similar. The proposed algorithm is validated extensively using public real-world datasets.

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“…They address the problems of scheduling and maneuvering cameras to observe targets based on their present positions. Likewise Capezio et al [4] develop Cyber Scout, an autonomous surveillance and investigation system to detect and track OoI. They use a network of all-terrain vehicles and focus on vision for inspection, autonomous navigation, and dynamic path planning.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Description of operations concerning the edge nodes (ENs) and the inner nodes (INs). 4. Simulations in NS2 in order to validate our assertion regarding the goals achieved by the proposed architecture.…”

Section: Introductionmentioning

confidence: 99%

VISTA: achieving cumulative VIsion through energy efficient Silhouette recognition of mobile Targets through collAboration of visual sensor nodes

et al. 2014

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Visual sensor networks (VSNs) are innovative networks founded on a broad range of areas such as networking, imaging, and database systems. These networks demand well-defined architectures in terms of sensor nodes and camera deployment, image capturing and processing, and well-organized distributed systems. This makes existing VSN architectures deficient because these are limited in approach and in design. In this paper, we propose VISTA, a distributed vision multi-layer architecture aimed at constructing the cumulative vision of mobile objects (MOs). VISTA realizes silhouette recognition of mobile targets through (a) pre-meditated deployment of sensor nodes (SNs) that are equipped with sonar sensors and fixed view (FV) on-board cameras present at the periphery of region of interest (RoI) and SNs with only on-board cameras within RoI, (b) pre-distribution of silhouettes of known objects across SNs, (c) sonar-based presence detection of MO at the outskirts of RoI, (d) MO silhouette capturing and matching at interior node to determine the % age match, (e) subsequent activation of next interior cameras in order to improve % age match, and (f) terminating further activation upon threshold recognition of MO. Experimental evaluation of our image processing algorithms against baseline algorithms with respect to execution time and memory shows significant reduction in image data and memory occupancy. Also, experiments show that true match is achieved fully under broad daylight conditions and large backgrounds when our proposed background subtraction and pixel reduction techniques are used. The mobility-driven behavior of associated network layer algorithms of VISTA is simulated in a network simulator (NS2) by representing the surety of MO identification as a function of number of cameras, database size and distribution, MO's trajectory, stored perspectives, and network depth. The simulation results show that doubling and, in some situations, manifold increase is observed in the surety of the target with an increase in the number of silhouettes deployed against the baselined database size and mobility model. The results substantiate that VISTA is a suitable architecture for low-cost, autonomous and efficient human and asset monitoring surveillance, friend-or-foe (FoF) identification, and target tracking systems.

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Robot-Assisted Surveillance in Large Environments

Cited by 8 publications

References 10 publications

Sparse optimization for robust and efficient loop closing

Sparse optimization for robust and efficient loop closing

An Online Sparsity-Cognizant Loop-Closure Algorithm for Visual Navigation

VISTA: achieving cumulative VIsion through energy efficient Silhouette recognition of mobile Targets through collAboration of visual sensor nodes

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