Multi-UAV collaborative monocular SLAM

Schmuck, Patrik

doi:10.1109/icra.2017.7989445

Cited by 171 publications

(110 citation statements)

References 17 publications

Supporting

Mentioning

109

Contrasting

Unclassified

Order By: Relevance

“…The data was used to perform sparse feature matching for robot localization, and to detect overlap in the sensor field of view of different robots. Recent results utilizing similar mapping frameworks were presented in [191], [192].…”

Section: Cooperative Aerial Mappingmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Section: Cooperative Aerial Mappingmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Current 3D reconstruction solutions running on smartphones only offer feedback to single users during image acquisitions, and do not yet seamlessly include collaborative approaches with simultaneous feedback to the multiple. The most common solution for collaborative mapping, based either on Simultaneous Localization and Mapping (SLAM) or SfM approaches, is to produce separate maps that are finally fused together (Forster et al, 2013;Untzelmann et al, 2013;Morrison et al, 2016;Schmuck, 2017). The procedure presented in this paper is based on an incremental SfM approach (Schonberger and Frahm, 2016), which updates and augments the global sparse 3D point cloud when a new image is uploaded.…”

Section: Related Work and Main Innovationsmentioning

confidence: 99%

3d Reconstruction With a Collaborative Approach Based on Smartphones and a Cloud-Based Server

Nocerino

Poiesi

Locher

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:The paper presents a collaborative image-based 3D reconstruction pipeline to perform image acquisition with a smartphone and geometric 3D reconstruction on a server during concurrent or disjoint acquisition sessions. Images are selected from the video feed of the smartphone's camera based on their quality and novelty. The smartphone's app provides on-the-fly reconstruction feedback to users co-involved in the acquisitions. The server is composed of an incremental SfM algorithm that processes the received images by seamlessly merging them into a single sparse point cloud using bundle adjustment. Dense image matching algorithm can be lunched to derive denser point clouds. The reconstruction details, experiments and performance evaluation are presented and discussed.

show abstract

“…Next to our method, maps are also generated using SLAM approaches that combine a VO or VIO front‐end with a bundle adjustment back‐end (e.g., Forster, Lynen, Kneip, & Scaramuzza, , ; Schmuck & Chli, ; T. Schneider et al, ). In the context of overview obstacle maps, a detailed benchmark comparison of the mapping accuracy and processing time requirements of our SfM‐based method against those algorithms would be of interest.…”

Section: Future Workmentioning

confidence: 99%

“…Next to our method, maps are also generated using SLAM approaches that combine a VO or VIO front-end with a bundle adjustment back-end (e.g., Forster, Lynen, Kneip, & Scaramuzza, 2013Schmuck & Chli, 2017;T. Schneider et al, 2018).…”

Section: Future Workmentioning

confidence: 99%

Overview obstacle maps for obstacle‐aware navigation of autonomous drones

Pestana

Maurer

Muschick

et al. 2019

Journal of Field Robotics

View full text Add to dashboard Cite

Achieving the autonomous deployment of aerial robots in unknown outdoor environments using only onboard computation is a challenging task. In this study, we have developed a solution to demonstrate the feasibility of autonomously deploying drones in unknown outdoor environments, with the main capability of providing an obstacle map of the area of interest in a short period of time. We focus on use cases where no obstacle maps are available beforehand, for instance, in search and rescue scenarios, and on increasing the autonomy of drones in such situations. Our vision‐based mapping approach consists of two separate steps. First, the drone performs an overview flight at a safe altitude acquiring overlapping nadir images, while creating a high‐quality sparse map of the environment by using a state‐of‐the‐art photogrammetry method. Second, this map is georeferenced, densified by fitting a mesh model and converted into an Octomap obstacle map, which can be continuously updated while performing a task of interest near the ground or in the vicinity of objects. The generation of the overview obstacle map is performed in almost real time on the onboard computer of the drone, a map of size 100m×75m is created in ≈2.75min, therefore, with enough time remaining for the drone to execute other tasks inside the area of interest during the same flight. We evaluate quantitatively the accuracy of the acquired map and the characteristics of the planned trajectories. We further demonstrate experimentally the safe navigation of the drone in an area mapped with our proposed approach.

show abstract

Multi-UAV collaborative monocular SLAM

Cited by 171 publications

References 17 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

3d Reconstruction With a Collaborative Approach Based on Smartphones and a Cloud-Based Server

Overview obstacle maps for obstacle‐aware navigation of autonomous drones

Contact Info

Product

Resources

About