Inter-Robot Range Measurements in Pose Graph Optimization

Boroson, Elizabeth R.; Hewitt, Robert A.; Ayanian, Nora; Croix, Jean-Pierre de la

doi:10.1109/iros45743.2020.9341227

Cited by 16 publications

(19 citation statements)

References 22 publications

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“…(Zhou and Roumeliotis, 2006) used a combination of direct and indirect detection approaches, where colored cylinders were installed to be detected by omnidirectional cameras. In addition, (Gentner et al, 2018;Boroson et al, 2020;Cao and Beltrame, 2021) propose to replace visual loop closures by Ultra-Wide Band (UWB) measurements from beacons onboard the robots. Given a few distance measurements provided by the UWB sensors, the robots can estimate their current relative pose with respect to each other and establish a common reference frame.…”

Section: Direct Inter-robot Loop Closuresmentioning

confidence: 99%

“…An interesting, yet still underdeveloped, trend is to leverage the communication medium for inter-robot measurements. This has been successfully done with UWB (Gentner et al, 2018;Boroson et al, 2020;Cao and Beltrame, 2021) or WiFi (Liu et al, 2020), and could be a promising avenue using multipath analysis with channel estimators in 5G networks (Ge et al, 2021). Future techniques might even sidestep inter-robot data transfer completely by communicating via sensor observations of each other and predetermined cues such as visual tags or behavioral patterns (Kim et al, 2010).…”

Section: Resilient Inter-robot Communicationmentioning

confidence: 99%

See 1 more Smart Citation

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: Direct Inter-robot Loop Closuresmentioning

confidence: 99%

Section: Resilient Inter-robot Communicationmentioning

confidence: 99%

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

Lajoie

Ramtoula

Wu³

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Significant attention has also been given to UWB as a sensing modality for robotic platforms. TrackIO (Dhekne et al, 2019) deploys a UAV-UWB system to estimate first responders' location and velocity even in deep indoor environments, while the autonomous Puffer project (Boroson et al, 2020) uses UWB for pose graph optimization. However, UWB typically needs to operate on wide bandwidths resulting in significant FCC restrictions on operating range and power (Mahfouz et al, 2009).…”

Section: Sensing Using Wireless Signalsmentioning

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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“…(Zhou and Roumeliotis, 2006) used a combination of direct and indirect detection approaches, where colored cylinders were installed to be detected by omnidirectional cameras. In addition, (Gentner et al, 2018;Boroson et al, 2020;Cao and Beltrame, 2020) propose to replace visual loop closures by Ultra-Wide Band (UWB) measurements from beacons onboard the robots. Given a few distance measurements provided by the UWB sensors, the robots can estimate their current relative pose with respect to each other and establish a common reference frame.…”

Section: Direct Inter-robot Loop Closuresmentioning

confidence: 99%

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

Lajoie,

Ramtoula,

et al. 2021

Preprint

View full text Add to dashboard Cite

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 multirobot 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.

show abstract

Inter-Robot Range Measurements in Pose Graph Optimization

Cited by 16 publications

References 22 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

A wireless signal-based sensing framework for robotics

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

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