Active SLAM using 3D Submap Saliency for Underwater Volumetric Exploration

Suresh, Sudharshan; Sodhi, Paloma; Mangelson, Joshua G.; Wettergreen, David; Kaess, Michael

doi:10.1109/icra40945.2020.9196939

Cited by 36 publications

(26 citation statements)

References 37 publications

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“…Most of the studied approaches assume the system operates in an indoor static environment, being ground and aerial robots the main actors in 2D and 3D ASLAM systems, respectively. There are some research works trying to solve the problem in other scenarios as well, such as in underwater environments [ 123 , 166 , 167 , 168 ].…”

Section: Discussionmentioning

confidence: 99%

“…Nonetheless, these exploration algorithms maximize coverage disregard the cumulative effect of the localization drift, which can lead to unrepresentative maps. For this reason, many works concur that the navigation policy of ASLAM systems should reduce uncertainty by balancing exploration actions and place revisiting actions [ 122 , 123 ]. The place revisiting topic is also known as loop closure and it is also inherited from SLAM.…”

Section: Place Revisitingmentioning

confidence: 99%

See 1 more Smart Citation

Active Mapping and Robot Exploration: A Survey

Lluvia

Lazkano

Ansuategi

2021

Sensors

100

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Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.

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Section: Discussionmentioning

confidence: 99%

Section: Place Revisitingmentioning

confidence: 99%

Active Mapping and Robot Exploration: A Survey

Lluvia

Lazkano

Ansuategi

2021

Sensors

100

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“…Subsequent work by Palomeras et al [24] extended the approach to 3D sonar-based mapping and exploration of cluttered underwater environments using a next-best-view framework that weighs travel distance, frontiers, and contourfollowing when selecting a new viewpoint. In addition to view planning, active SLAM has been applied to underwater robots, used to incorporate planned detours into visual inspections that achieve loop closures [25], [26], [27], and to deviate from three-dimensional sonar-based next-best-view planning to achieve a loop closure when localization uncertainty exceeds a designated threshold [28]. Of these two works, the former navigates at a fixed standoff from a ship hull's surface, and the latter explores and maps an indoor tank environment.…”

Section: Related Workmentioning

confidence: 99%

“…In the sections that follow, we will describe our proposed EM exploration framework and its application to underwater mapping with sonar, demonstrating that it achieves far lower localization and mapping errors than frontier-based [10] and next-best-view [16] exploration algorithms, and that the localization and mapping errors realized are comparable to that of [28], while achieving a superior rate of exploration.…”

Section: Related Workmentioning

confidence: 99%

Virtual Maps for Autonomous Exploration of Cluttered Underwater Environments

Wang¹,

Chen²,

Huang³

et al. 2022

Preprint

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We consider the problem of autonomous mobile robot exploration in an unknown environment, taking into account a robot's coverage rate, map uncertainty, and state estimation uncertainty. This paper presents a novel exploration framework for underwater robots operating in cluttered environments, built upon simultaneous localization and mapping (SLAM) with imaging sonar. The proposed system comprises path generation, place recognition forecasting, belief propagation and utility evaluation using a virtual map, which estimates the uncertainty associated with map cells throughout a robot's workspace. We evaluate the performance of this framework in simulated experiments, showing that our algorithm maintains a high coverage rate during exploration while also maintaining low mapping and localization error. The real-world applicability of our framework is also demonstrated on an underwater remotely operated vehicle (ROV) exploring a harbor environment.

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“…Suresh et al focus on volumetric exploration of 3D underwater environments with multibeam sonar Suresh et al (2020). In order to identify the next location to revisit, a 3D visual dictionary is built from real-world sonar data and a metric of submap saliency is computed.…”

Section: A Brief Glance At Active Slammentioning

confidence: 99%

AUV localisation: a review of passive and active techniques

Maurelli

Krupínski

Xia

et al. 2021

Int J Intell Robot Appl

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Localisation, i.e. estimation of one’s position in a given environment is a crucial element of many mobile systems, manned and unmanned. Due to the high demand for autonomous exploration, patrolling and inspection services and a rapid improvement of batteries, sensors and machine learning algorithms, the quality of localisation becomes even more important for smart robotic systems. The underwater domain is a very challenging environment due to the water blocking most of the signals over short distances. Recent results in localisation techniques for underwater vehicles are summarised in two principal categories: passive techniques, which strive to provide the best estimation of the vehicle’s position (global or local) given the past and current information from sensors, and active techniques, which additionally produce guidance output that is expected to minimise the uncertainty of estimated position.

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Active SLAM using 3D Submap Saliency for Underwater Volumetric Exploration

Cited by 36 publications

References 37 publications

Active Mapping and Robot Exploration: A Survey

Active Mapping and Robot Exploration: A Survey

Virtual Maps for Autonomous Exploration of Cluttered Underwater Environments

AUV localisation: a review of passive and active techniques

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