COVINS: Visual-Inertial SLAM for Centralized Collaboration

Schmuck, Patrik; Ziegler, Thomas; Karrer, Marco; Perraudin, Jonathan; Chli, Margarita

doi:10.1109/ismar-adjunct54149.2021.00043

Cited by 57 publications

(28 citation statements)

References 19 publications

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“…In this work, we focus on collaborative BA (Example 1) in our experimental validation (Sec. VI), due to its fundamental role in multi-robot visual SLAM [1]- [4]. However, we note that our approach extends beyond the above examples to many other multi-agent estimation problems that can be described with a factor graph [32].…”

Section: Collaborative Geometric Estimationmentioning

confidence: 90%

“…We generate noisy inputs for each dataset by perturbing the ORB-SLAM3 estimates by zero-mean Gaussian noise. 4 We compare LARPG against two baseline methods that can be implemented under the communication architecture considered in this work. The first baseline is the method in [26] using distributed preconditioned conjugate gradient (PCG).…”

Section: B Performance On Collaborative Slam Datasetsmentioning

confidence: 99%

“…Existing multi-agent systems often offload the aforementioned global optimization, such as bundle adjustment (BA), to a central server or base station [1]- [4]. However, as the number of robots or mission time increases, centralized optimization suffers from increasing problem size that eventually makes the server a computational bottleneck.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation

Tian¹,

Bedi²,

Koppel³

et al. 2022

Preprint

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We present the first distributed optimization algorithm with lazy communication for collaborative geometric estimation, the backbone of modern collaborative simultaneous localization and mapping (SLAM) and structure-from-motion (SfM) applications. Our method allows agents to cooperatively reconstruct a shared geometric model on a central server by fusing individual observations, but without the need to transmit potentially sensitive information about the agents themselves (such as their locations). Furthermore, to alleviate the burden of communication during iterative optimization, we design a set of communication triggering conditions that enable agents to selectively upload local information that are useful to global optimization. Our approach thus achieves significant communication reduction with minimal impact on optimization performance. As our main theoretical contribution, we prove that our method converges to first-order critical points with a sublinear convergence rate. Numerical evaluations on bundle adjustment problems from collaborative SLAM and SfM datasets show that our method performs competitively against existing distributed techniques, while achieving up to 78% total communication reduction.

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Section: Collaborative Geometric Estimationmentioning

confidence: 90%

Section: B Performance On Collaborative Slam Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation

Tian¹,

Bedi²,

Koppel³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, some solutions deployed in large-scale environments during the DARPA Subterranean Challenge (Hudson et al, 2021;Agha et al, 2021) led to the developments of new C-SLAM systems, such as the robust lidar-based approach of (Ebadi et al, 2020). Alternatively, (Schmuck et al, 2021) proposes a vision-based centralized C-SLAM system incorporating inertial measurements, which has been tested with up to 12 robots in simulation. In another line of work, (Lajoie et al, 2020) presents a distributed and decentralized system robust to spurious measurements, along with online experiments on real robots, and a publicly available implementation.…”

Section: Complete C-slam Systemsmentioning

confidence: 99%

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Lajoie

Ramtoula

Wu³

et al. 2022

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Motivated by the tremendous progress, we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area’s current trends and promising research avenues.

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“…proposed a centralized multi-intelligence collaborative monocular visual-inertial SLAM deployed on multiple iOS mobile devices. COVINS(Schmuck et al, 2021) is a novel collaborative SLAM system that supports multi-agent, scalable SLAM in large environments and for large teams of more than ten agents.…”

mentioning

confidence: 99%

Advanced Visual SLAM and Image Segmentation Techniques for Augmented Reality

Jiang

Tran

Williams

2022

International Journal of Virtual and Augmented Reality

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Augmented reality can enhance human perception to experience a virtual-reality intertwined world by computer vision techniques. However, the basic techniques cannot handle complex large-scale scenes, tackle real-time occlusion, and render virtual objects in augmented reality. Therefore, this paper studies potential solutions, such as visual SLAM and image segmentation, that can address these challenges in the augmented reality visualizations. This paper provides a review of advanced visual SLAM and image segmentation techniques for augmented reality. In addition, applications of machine learning techniques for improving augmented reality are presented.

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COVINS: Visual-Inertial SLAM for Centralized Collaboration

Cited by 57 publications

References 19 publications

Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation

Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Advanced Visual SLAM and Image Segmentation Techniques for Augmented Reality

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