Visual summaries for low-bandwidth semantic mapping with autonomous underwater vehicles

Kaeli, Jeffrey W.; Leonard, John J.; Singh, Hanumant

doi:10.1109/auv.2014.7054429

Cited by 13 publications

(9 citation statements)

References 28 publications

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“…Previous versions of BNP-ROST have demonstrated the ability to produce useful semantic maps of real-world environments while running on real robot hardware [19]. Furthermore, the compressed semantic maps and topic descriptors shared between robots are very small (typically around 10 to 100 kB) so they can be transmitted between robots in even severely bandwidth-constrained environments, like the deep sea [19], [40].…”

Section: Resultsmentioning

confidence: 99%

Multi-Robot Distributed Semantic Mapping in Unfamiliar Environments through Online Matching of Learned Representations

Jamieson¹,

Fathian²,

Khosoussi³

et al. 2021

Preprint

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We present a solution to multi-robot distributed semantic mapping of novel and unfamiliar environments. Most state-of-the-art semantic mapping systems are based on supervised learning algorithms that cannot classify novel observations online. While unsupervised learning algorithms can invent labels for novel observations, approaches to detect when multiple robots have independently developed their own labels for the same new class are prone to erroneous or inconsistent matches. These issues worsen as the number of robots in the system increases and prevent fusing the local maps produced by each robot into a consistent global map, which is crucial for cooperative planning and joint mission summarization. Our proposed solution overcomes these obstacles by having each robot learn an unsupervised semantic scene model online and use a multiway matching algorithm to identify consistent sets of matches between learned semantic labels belonging to different robots. Compared to the state of the art, the proposed solution produces 20-60% higher quality global maps that do not degrade even as many more local maps are fused. OPEN SOURCE SOFTWAREAn implementation of this solution is contributed in the "Sunshine" 3D semantic mapping ROS package, a general purpose single-and multi-robot semantic mapping system: https://gitlab.com/warplab/ros/sunshine.

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Section: Resultsmentioning

confidence: 99%

Multi-Robot Distributed Semantic Mapping in Unfamiliar Environments through Online Matching of Learned Representations

Jamieson¹,

Fathian²,

Khosoussi³

et al. 2021

Preprint

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“…2 h for common deployments). Additional image selection strategies, automated pre-clustering and visual overview displays 14 , 15 should be implemented in the future to speed up the subsequent semi-automated exploration of data.…”

Section: Discussionmentioning

confidence: 99%

An acquisition, curation and management workflow for sustainable, terabyte-scale marine image analysis

2018

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Optical imaging is a common technique in ocean research. Diving robots, towed cameras, drop-cameras and TV-guided sampling gear: all produce image data of the underwater environment. Technological advances like 4K cameras, autonomous robots, high-capacity batteries and LED lighting now allow systematic optical monitoring at large spatial scale and shorter time but with increased data volume and velocity. Volume and velocity are further increased by growing fleets and emerging swarms of autonomous vehicles creating big data sets in parallel. This generates a need for automated data processing to harvest maximum information. Systematic data analysis benefits from calibrated, geo-referenced data with clear metadata description, particularly for machine vision and machine learning. Hence, the expensive data acquisition must be documented, data should be curated as soon as possible, backed up and made publicly available. Here, we present a workflow towards sustainable marine image analysis. We describe guidelines for data acquisition, curation and management and apply it to the use case of a multi-terabyte deep-sea data set acquired by an autonomous underwater vehicle.

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“…At this point, it is worth asking why there is need for to go beyond Level 0 in Table 1 [10,56]. Camera imagery is often the primary sensing modality, but transmitting images from the robot platform to the scientists running the mission can be very expensive in terms of time and energy usage.…”

Section: The Need For Human-robot Cooperationmentioning

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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Visual summaries for low-bandwidth semantic mapping with autonomous underwater vehicles

Cited by 13 publications

References 28 publications

Multi-Robot Distributed Semantic Mapping in Unfamiliar Environments through Online Matching of Learned Representations

Multi-Robot Distributed Semantic Mapping in Unfamiliar Environments through Online Matching of Learned Representations

An acquisition, curation and management workflow for sustainable, terabyte-scale marine image analysis

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

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