Active Rendezvous for Multi-robot Pose Graph Optimization Using Sensing over Wi-Fi

Wang, Weiying; Jadhav, Ninad; Vohs, Paul; Hughes, Nathan; Mazumder, Mark; Gil, Stephanie

doi:10.1007/978-3-030-95459-8_51

Cited by 13 publications

(10 citation statements)

References 26 publications

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“…We observe that more exciting new directions are still being discovered, considering that recent approaches such as (Tian et al, 2021b) have been shown to outperform, both in accuracy and convergence rate, the well established Distributed Gauss-Seidel pose graph optimization method (Choudhary et al, 2017a) reused in many state-of-the-art C-SLAM systems such as (Cieslewski et al, 2018;Lajoie et al, 2020;Wang et al, 2019). Those promising approaches also include the majorization-minimization technique from (Fan and Murphey, 2020), the consensus-based 3D pose estimation technique inspired by distributed formation control from (Cristofalo et al, 2019;Cristofalo et al, 2020), and (Zhu et al, 2021) distributed estimator based on covariance intersection.…”

Section: Other Estimation Techniquesmentioning

confidence: 99%

“…While RANSAC works well in centralized settings, it is not adapted to distributed systems. Therefore, researchers recently explored other ways of detecting outliers such as leveraging extra information from the wireless communication channels during a rendezvous between two robots (Wang et al, 2019). Since such approaches work only for direct inter-robot loop closures, there is a need for general robust data association in the back-end.…”

Section: Perceptual Aliasing Mitigationmentioning

confidence: 99%

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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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Section: Other Estimation Techniquesmentioning

confidence: 99%

Section: Perceptual Aliasing Mitigationmentioning

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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“…This approach requires only to share the latest (2D or 3D) pose estimates involved in the inter-robot measurements. Recent distributed SLAM solutions [9] and [20] have used the implementation of Choudhary et al [3] as back-end for their experiments. While here we focus on PGO, we refer the reader to [3] for an extensive review on other distributed estimation techniques.…”

Section: A Distributed Pose Graph Optimization (Pgo)mentioning

confidence: 99%

“…Dong et al [16] adopt expectation maximization for robust multi-robot PGO. Wang et al [20] use wireless channel information to detect potential outliers during a multi-robot rendezvous.…”

Section: B Robust Pgomentioning

confidence: 99%

DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams

Lajoie

Ramtoula

Chang

et al. 2020

IEEE Robot. Autom. Lett.

172

104

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To achieve collaborative tasks, robots in a team need to have a shared understanding of the environment and their location within it. Distributed Simultaneous Localization and Mapping (SLAM) offers a practical solution to localize the robots without relying on an external positioning system (e.g. GPS) and with minimal information exchange. Unfortunately, current distributed SLAM systems are vulnerable to perception outliers and therefore tend to use very conservative parameters for inter-robot place recognition. However, being too conservative comes at the cost of rejecting many valid loop closure candidates, which results in less accurate trajectory estimates. This paper introduces DOOR-SLAM, a fully distributed SLAM system with an outlier rejection mechanism that can work with less conservative parameters. DOOR-SLAM is based on peerto-peer communication and does not require full connectivity among the robots. DOOR-SLAM includes two key modules: a pose graph optimizer combined with a distributed pairwise consistent measurement set maximization algorithm to reject spurious inter-robot loop closures; and a distributed SLAM front-end that detects inter-robot loop closures without exchanging raw sensor data. The system has been evaluated in simulations, benchmarking datasets, and field experiments, including tests in GPS-denied subterranean environments. DOOR-SLAM produces more inter-robot loop closures, successfully rejects outliers, and results in accurate trajectory estimates, while requiring low communication bandwidth. Full source code is available at https://github.com/MISTLab/DOOR-SLAM.git.

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“…other robots in the team, allowing for more reliable communication in complex and cluttered environments (Gil et al, 2015a;Wang et al, 2019); and (iii) the full AOA profile can be used as a signal multipath signature of a robot to verify its uniqueness, with implications for security and authentication in multi-agent systems (Gil et al, 2015b;Xiong and Jamieson 2013b). However, the main limitation of many of these approaches is that they do not extend to general robotics platforms and full 3D mobility of robots.…”

Section: Introductionmentioning

confidence: 99%

A wireless signal-based sensing framework for robotics

Jadhav

Wang

Zhang

et al. 2022

The International Journal of Robotics Research

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In this paper, we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging a robots’ mobility in 3D space. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots’ local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of (i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and (ii) a Cramer–Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOA estimation as a function of the geometry of robot motion. This is a critical distinction with previous work on SAR-based methods that restrict robot mobility to prescribed motion patterns, do not generalize to the full 3D space, and require transmitting robots to be stationary during data acquisition periods. We show that allowing robots to use their full mobility in 3D space while performing SAR results in more accurate AOA profiles and thus better AOA estimation. We formally characterize this observation as the informativeness of the robots’ motion, a computable quantity for which we derive a closed form. All analytical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms using 5 GHz WiFi. Our experimental results bolster our analytical findings, demonstrating that 3D motion provides enhanced and consistent accuracy, with a total AOA error of less than 10◦ for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA and validate this theory empirically using robot displacements obtained using an off-the-shelf Intel Tracking Camera T265. Finally, we demonstrate the performance of our system on a multi-robot task where a heterogeneous air/ground pair of robots continuously measure AOA profiles over a WiFi link to achieve dynamic rendezvous in an unmapped, 300 m2 environment with occlusions.

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Active Rendezvous for Multi-robot Pose Graph Optimization Using Sensing over Wi-Fi

Cited by 13 publications

References 26 publications

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

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

DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams

A wireless signal-based sensing framework for robotics

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