Large scale multiple robot visual mapping with heterogeneous landmarks in semi-structured terrain

Vidal-Calleja, Teresa; Berger, Cyrille; Solí, Joan; Lacroix, Simon

doi:10.1016/j.robot.2011.05.008

Cited by 64 publications

(49 citation statements)

References 46 publications

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“…The main constraint comes from the fact that submaps cannot be updated and are only sent once on the network. The work of Vidal-Calleja et al in [226] also proposes an extension of submap SLAM, and more specifically of the hierarchical approach introduced in [31]. Only the topological map is exchanged inside the fleet and a global metric map cannot be recovered on the fly.…”

Section: B Decentralized Slammentioning

confidence: 99%

“…In [238], the authors propose to exchange maps only when vehicles can detect each other, which means that big amount of data will be sent punctually. Methods communicating only topological maps, like in [226] and [227], avoid this problem by providing very light maps.…”

Section: B Decentralized Slammentioning

confidence: 99%

“…Some approaches have been demonstrated indoor in real time [242] [233]. In [226], the authors demonstrate an air/ground cooperation in outdoor environment. The map is built in a cooperative fashion with vision landmarks.…”

Section: B Decentralized Slammentioning

confidence: 99%

See 2 more Smart Citations

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

628

366

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Abstract-In this article, we propose a survey of the Simultaneous Localization And Mapping field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multi-vehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various largescale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

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Section: B Decentralized Slammentioning

confidence: 99%

Section: B Decentralized Slammentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

628

366

View full text Add to dashboard Cite

show abstract

“…Points are usually augmented with descriptors for feature matching purposes. By matching 3-D points and lines, robots can estimate their relative poses and fuse their local maps to maintain geometric consistency and achieve effective cooperation in large-scale environments [189]. Robots may also maintain the position uncertainty of each point in the map for handling of dynamic objects [190].…”

Section: Cooperative Aerial Mappingmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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“…In most related works, the aerial robot flies outdoors higher than 20 meters, and can be localized using GPS [7]- [9]. To the best of our knowledge, the work in [1] is the first to demonstrate how a MAV could assist a ground robot in close collaboration in mapping a damaged building indoors.…”

Section: Related Workmentioning

confidence: 99%

Air-ground localization and map augmentation using monocular dense reconstruction

Förster

Pizzoli

Scaramuzza

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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We propose a new method for the localization of a Micro Aerial Vehicle (MAV) with respect to a ground robot. We solve the problem of registering the 3D maps computed by the robots using different sensors: a dense 3D reconstruction from the MAV monocular camera is aligned with the map computed from the depth sensor on the ground robot. Once aligned, the dense reconstruction from the MAV is used to augment the map computed by the ground robot, by extending it with the information conveyed by the aerial views. The overall approach is novel, as it builds on recent developments in live dense reconstruction from moving cameras to address the problem of air-ground localization. The core of our contribution is constituted by a novel algorithm integrating dense reconstructions from monocular views, Monte Carlo localization, and an iterative pose refinement. In spite of the radically different vantage points from which the maps are acquired, the proposed method achieves high accuracy whereas appearance-based, state-of-the-art approaches fail. Experimental validation in indoor and outdoor scenarios reported an accuracy in position estimation of 0.08 meters and real time performance. This demonstrates that our new approach effectively overcomes the limitations imposed by the difference in sensors and vantage points that negatively affect previous techniques relying on matching visual features. Air-Ground Localization and Map Augmentation Using Monocular Dense ReconstructionChristian Forster, Matia Pizzoli, Davide ScaramuzzaAbstract-We propose a new method for the localization of a Micro Aerial Vehicle (MAV) with respect to a ground robot. We solve the problem of registering the 3D maps computed by the robots using different sensors: a dense 3D reconstruction from the MAV monocular camera is aligned with the map computed from the depth sensor on the ground robot. Once aligned, the dense reconstruction from the MAV is used to augment the map computed by the ground robot, by extending it with the information conveyed by the aerial views. The overall approach is novel, as it builds on recent developments in live dense reconstruction from moving cameras to address the problem of airground localization. The core of our contribution is constituted by a novel algorithm integrating dense reconstructions from monocular views, Monte Carlo localization, and an iterative pose refinement. In spite of the radically different vantage points from which the maps are acquired, the proposed method achieves high accuracy whereas appearance-based, state-of-theart approaches fail. Experimental validation in indoor and outdoor scenarios reported an accuracy in position estimation of 0.08 meters and real time performance. This demonstrates that our new approach effectively overcomes the limitations imposed by the difference in sensors and vantage points that negatively affect previous techniques relying on matching visual features.

show abstract

Large scale multiple robot visual mapping with heterogeneous landmarks in semi-structured terrain

Cited by 64 publications

References 46 publications

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

A Survey on Aerial Swarm Robotics

Air-ground localization and map augmentation using monocular dense reconstruction

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