SegMap: 3D Segment Mapping using Data-Driven Descriptors
TL;DR: This paper presents SegMap: a map representation solution to the localization and mapping problem based on the extraction of segments in 3D point clouds that addresses the data compression requirements of real-time single- and multi-robot systems.
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Abstract: When performing localization and mapping, working at the level of structure can be advantageous in terms of robustness to environmental changes and differences in illumination. This paper presents SegMap: a map representation solution to the localization and mapping problem based on the extraction of segments in 3D point clouds. In addition to facilitating the computationally intensive task of processing 3D point clouds, working at the level of segments addresses the data compression requirements of real-time single- and multi-robot systems. While current methods extract descriptors for the single task of localization, SegMap leverages a data-driven descriptor in order to extract meaningful features that can also be used for reconstructing a dense 3D map of the environment and for extracting semantic information. This is particularly interesting for navigation tasks and for providing visual feedback to end-users such as robot operators, for example in search and rescue scenarios. These capabilities are demonstrated in multiple urban driving and search and rescue experiments. Our method leads to an increase of area under the ROC curve of 28.3% over current state of the art using eigenvalue based features. We also obtain very similar reconstruction capabilities to a model specifically trained for this task. The SegMap implementation will be made available open-source along with easy to run demonstrations at this http URL. A video demonstration is available at this https URL.
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Citations
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
Antoni Rosinol,Marcus Abate,Yun Chang,Luca Carlone +3 more
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TL;DR: Kimera as discussed by the authors is an open-source C++ library for real-time metric-semantic visual-inertial SLAM by enabling mesh reconstruction and semantic labeling in 3D.
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SuMa++: Efficient LiDAR-based Semantic SLAM
Xieyuanli Chen,Andres Milioto,Emanuele Palazzolo,Philippe Giguère,Jens Behley,Cyrill Stachniss +5 more
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TL;DR: An extension of a recently published surfel-based mapping approach exploiting three-dimensional laser range scans by integrating semantic information to facilitate the mapping process, which enables us to reliably filter moving objects, but also improve the projective scan matching via semantic constraints.
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The current state and future outlook of rescue robotics
Jeffrey A. Delmerico,Stefano Mintchev,Alessandro Giusti,Gromov Boris A,Kamilo Melo,Tomislav Horvat,Cesar Cadena,Marco Hutter,Auke Jan Ijspeert,Dario Floreano,Luca Maria Gambardella,Roland Siegwart,Davide Scaramuzza +12 more
TL;DR: The current state of the art in ground and aerial robots, marine and amphibious systems, and human–robot control interfaces are surveyed and the readiness of these technologies with respect to the needs of first responders and disaster recovery efforts is assessed.
LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments
Kamak Ebadi,Yun Chang,Matteo Palieri,Alex Stephens,Alex Hatteland,Eric Heiden,Abhishek Thakur,Nobuhiro Funabiki,Benjamin Morrell,Sally L. Wood,Luca Carlone,Ali-akbar Agha-mohammadi +11 more
- 01 May 2020
TL;DR: This paper presents a system architecture to enhance subterranean operation, including an accurate lidar-based front-end, and a flexible and robust back-end that automatically rejects outlying loop closures, and discusses potential improvements, limitations of the state of the art, and future research directions.
SegMap: Segment-based mapping and localization using data-driven descriptors:
Renaud Dubé,Andrei Cramariuc,Daniel Dugas,Hannes Sommer,Marcin Dymczyk,Juan Nieto,Roland Siegwart,Cesar Cadena +7 more
TL;DR: SegMap is presented: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds that achieves a higher localization accuracy and a 6% increase in recall over state-of-the-art handcrafted descriptors.
174
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