CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multiview Data Association

Fathian, Kaveh; Khosoussi, Kasra; Tian, Yulun; C., Lusk, Parker; How, Jonathan P.

doi:10.1109/tro.2020.3002432

Cited by 25 publications

(32 citation statements)

References 25 publications

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“…In practice, however, CG matching can be made real-time by storing and reusing previously computed matches. On the other hand, the CLEAR algorithm could also be accelerated by implementing a block SVD method that exploits the separable structure of the underlying data association graph (Fathian et al, 2019). Still, as the number of submaps grows, we note that multiway association is likely to become the computation bottleneck of the overall system.…”

Section: Real-time Planning and Cslam Resultsmentioning

confidence: 99%

“…Similarly in the computer vision community, cycle consistency has also gained considerable attention owing to popular applications such as multi-shape matching (Huang and Guibas, 2013;Nguyen et al, 2011) and multi-image matching (Leonardos et al, 2017;Zhou et al, 2015). Principled approaches based on spectral relaxation (Pachauri et al, 2013), semidefinite relaxation (Chen et al, 2014), distributed consensus (Leonardos et al, 2017), and spectral clustering (Fathian et al, 2019) have been proposed for solving this problem, and performance guarantees for exact matching are proved under certain noise models (Chen et al, 2014;Pachauri et al, 2013). In this work, we propose a novel application of cycle consistent multiway matching for fusing tree landmarks during multi-robot data association and SLAM.…”

Section: Multiway Methodsmentioning

confidence: 99%

“…Among all algorithms proposed for cycle consistent multiway matching, we choose CLEAR (consistent lifting, embedding, and alignment rectification) proposed by Fathian et al (2019) for its high precision and superior speed. CLEAR takes as input the aggregate matrix,…”

Section: Globally Consistent Loop Closuresmentioning

confidence: 99%

“…When computing the embedding vectors, we use a block singular value decomposition (SVD) method that leverages the separable structure of the underlying data association graph. To further accelerate computation, we also implemented the greedy method for clustering as suggested in Fathian et al (2019).…”

Section: Globally Consistent Loop Closuresmentioning

confidence: 99%

See 3 more Smart Citations

Search and rescue under the forest canopy using multiple UAVs

Tian

Liu

et al. 2020

The International Journal of Robotics Research

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We present a multi-robot system for GPS-denied search and rescue under the forest canopy. Forests are particularly challenging environments for collaborative exploration and mapping, in large part due to the existence of severe perceptual aliasing which hinders reliable loop closure detection for mutual localization and map fusion. Our proposed system features unmanned aerial vehicles (UAVs) that perform onboard sensing, estimation, and planning. When communication is available, each UAV transmits compressed tree-based submaps to a central ground station for collaborative simultaneous localization and mapping (CSLAM). To overcome high measurement noise and perceptual aliasing, we use the local configuration of a group of trees as a distinctive feature for robust loop closure detection. Furthermore, we propose a novel procedure based on cycle consistent multiway matching to recover from incorrect pairwise data associations. The returned global data association is guaranteed to be cycle consistent, and is shown to improve both precision and recall compared with the input pairwise associations. The proposed multi-UAV system is validated both in simulation and during real-world collaborative exploration missions at NASA Langley Research Center.

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Section: Real-time Planning and Cslam Resultsmentioning

confidence: 99%

Section: Multiway Methodsmentioning

confidence: 99%

Section: Globally Consistent Loop Closuresmentioning

confidence: 99%

Section: Globally Consistent Loop Closuresmentioning

confidence: 99%

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Search and rescue under the forest canopy using multiple UAVs

Tian

Liu

et al. 2020

The International Journal of Robotics Research

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“…This enables the robots to communicate labels and observations among each other, and potentially still merge topic models if appropriate, with fewer drawbacks and less communication required. Of particular interest is using the CLEAR algorithm [33], which is capable of efficiently solving the multi-agent data association problem to find all sets of equivalent semantic labels across all robots, to find much better correspondences than the 1-to-1 correspondences solved using the Hungarian algorithm in [23].…”

Section: Multi-robot Federated Explorationmentioning

confidence: 99%

Enabling human-robot cooperation in scientific exploration of bandwidth-limited environments

Jamieson¹

2020

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None of this work would have been possible without the guidance and support of my co-supervisors, Dr. Yogesh Girdhar and Prof. Jonathan How. They have been exceedingly generous in giving their time, encouragement, and advice in matters both technical and related to professional development. I feel very grateful for the combined breadth and depth of their backgrounds, experiences, and technical knowledge, as this has been instrumental in allowing me to pursue such a cross-disciplinary and complex topic as co-robotic scientific exploration. I would next like to thank all of my collaborators and peers involved in the research and discussion of the work and ideas presented in this thesis. My fellow members of the WARPLab and the Autonomous Controls Lab always created a positive, supportive, and productive atmosphere to work in, one I missed when working from home due to current world affairs. I'd like to particularly thank Vv, John, Genevieve, Victoria, Mike, Kaveh, Kasra, and Kevin for many long discussions and useful advice on the topics and ideas presented in this work. I'd also like to thank Nathan, Brian, and Stefano for their invaluable technical advice and support. Alongside them are all the others at MIT, WHOI, and in the MIT-WHOI Joint Program who have been excellent friends to me since coming to MA and made me feel at home in a new country. Lastly, I would like to thank my family. In particular, my partner Victoria who provides me with constant support and inspires me with her incredible work ethic and dedication, as well my mom and dad and my brother Chris, whose love and encouragement have been ever-present and invaluable. I'd also like to thank Suzy and the rest of my extended family, who have likewise been major sources of support in both the best and worst of times. Finally, I dedicate this thesis in memory of my grandparents Chris & Molly, who were each deeply loved and are deeply missed; they always brought out the very best in me.

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Search and Rescue Under the Forest Canopy Using Multiple UAS

Tian

Liu

et al. 2020

Springer Proceedings in Advanced Robotics

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We present a multi-robot system for GPS-denied search and rescue under the forest canopy. Forests are particularly challenging environments for collaborative exploration and mapping, in large part due to the existence of severe perceptual aliasing which hinders reliable loop closure detection for mutual localization and map fusion. Our proposed system features unmanned aerial vehicles (UAVs) that perform onboard sensing, estimation, and planning. When communication is available, each UAV transmits compressed tree-based submaps to a central ground station for collaborative simultaneous localization and mapping (CSLAM). To overcome high measurement noise and perceptual aliasing, we use the local configuration of a group of trees as a distinctive feature for robust loop closure detection. Furthermore, we propose a novel procedure based on cycle consistent multiway matching to recover from incorrect pairwise data associations. The returned global data association is guaranteed to be cycle consistent, and is shown to improve both precision and recall compared to the input pairwise associations. The proposed multi-UAV system is validated both in simulation and during real-world collaborative exploration missions at NASA Langley Research Center.

show abstract

CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multiview Data Association

Cited by 25 publications

References 25 publications

Search and rescue under the forest canopy using multiple UAVs

Search and rescue under the forest canopy using multiple UAVs

Enabling human-robot cooperation in scientific exploration of bandwidth-limited environments

Search and Rescue Under the Forest Canopy Using Multiple UAS

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