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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
2019 The international journal of robotics research  
SegMap exploits a single compact data-driven descriptor for performing multiple tasks: global localization, 3D dense map reconstruction, and semantic information extraction.  ...  We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds.  ...  In white we show the local segments extracted from the robots' vicinity and characterized using our compact data-driven descriptor.  ... 
doi:10.1177/0278364919863090 fatcat:qllqpzsy7fbllccrt4u5nz2cgm

SegMap: 3D Segment Mapping using Data-Driven Descriptors

Renaud Dubé, Andrei Cramariuc, Daniel Dugas, Juan Nieto, Roland Siegwart, Cesar Cadena
2018 Robotics: Science and Systems XIV  
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  ...  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 white we show the local segments extracted from the robots' vicinity and characterized using our compact data-driven descriptor.  ... 
doi:10.15607/rss.2018.xiv.003 dblp:conf/rss/DubeCD0SC18 fatcat:so7lmix4ibglrlxcp2777rpc3e

LOL: Lidar-Only Odometry and Localization in 3D Point Cloud Maps [article]

David Rozenberszki, Andras Majdik
2020 arXiv   pre-print
In this paper we deal with the problem of odometry and localization for Lidar-equipped vehicles driving in urban environments, where a premade target map exists to localize against.  ...  the a priori offline map.  ...  Zeng et al. proposed the 3DMatch in [23] , one of the first data-driven CNN for localization in the 3D segment space.  ... 
arXiv:2007.01595v1 fatcat:nyxdogpq7vhybameud2oqi2oee

Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU

Georgi Tinchev, Adrian Penate-Sanchez, Maurice Fallon
2019 IEEE Robotics and Automation Letters  
In this work we explore laser-based localization in both urban and natural environments, which is suitable for online applications.  ...  The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations.  ...  We provide comparisons against a popular method for segment-based localization, SegMatch 1 , as proposed in [5] , a data-driven incremental approach SegMap 1 [6] , and our previous work NSM [7] .  ... 
doi:10.1109/lra.2019.2895264 fatcat:u2b3afekjvaelgqjwmwmrc57sq

ClusterMap Building and Relocalization in Urban Environments for Unmanned Vehicles

Zhichen Pan, Haoyao Chen, Silin Li, Yunhui Liu
2019 Sensors  
A location descriptor associated with each cluster is designed for differentiation. The relocalization in the global map is achieved by matching cluster descriptors between local and global maps.  ...  Map building and map-based relocalization techniques are important for unmanned vehicles operating in urban environments.  ...  Acknowledgments: We would thank Pengpeng Su, Wenqiang Chen, and Renxiao Liang for their technical support. Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/s19194252 fatcat:svq2dlfzpjgstiiehypaqj5pfm

Positioning and perception in LIDAR point clouds

Csaba Benedek, Andras Majdik, Balazs Nagy, Zoltan Rozsa, Tamas Sziranyi
2021 Digital signal processing (Print)  
Acknowledgment The research was supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Autonomous Systems National Laboratory Program.  ...  SegMap computes a data-driven compact descriptor to extract distinctive and meaningful features from point cloud segments in order to identify loop-closure situations along the trajectory.  ...  segmented MLS based map.  ... 
doi:10.1016/j.dsp.2021.103193 fatcat:assc6y3epfc5zjcke5pikfs6ea

Semantically Assisted Loop Closure in SLAM Using NDT Histograms

Anestis Zaganidis, Alexandros Zerntev, Tom Duckett, Grzegorz Cielniak
2019 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
We experimentally demonstrate on sequences from the KITTI benchmark that the map descriptor we propose outperforms NDT Histograms without semantics, and we validate its use on a SLAM task.  ...  In this work we extend the method for loop closure detection, using the labels already available from local registration into NDT Histograms, and we present a SLAM pipeline based on Semantic assisted NDT  ...  Regarding mapping and map representations, Segment mapping using data-driven descriptors (SegMap) is an algorithm that segments the scene incrementally as the robot moves, and then passes the segments  ... 
doi:10.1109/iros40897.2019.8968140 dblp:conf/iros/ZaganidisZDC19 fatcat:xkl7araxgrf6xnp4p5dkeptitu

A Survey on 3D LiDAR Localization for Autonomous Vehicles [article]

Mahdi Elhousni, Xinming Huang
2020 arXiv   pre-print
LiDARs are able to produce rich, dense and precise spatial data, which can tremendously help in localizing and tracking a moving vehicle.  ...  In this paper, we review the latest finding in 3D LiDAR localization for autonomous driving cars, and analyse the results obtained by each method, in an effort to guide the research community towards the  ...  The main contribution of this approach is its data driven 3D segment descriptor which is extracted using a network composed of a series of convolutional and fully connected layers.  ... 
arXiv:2006.00648v2 fatcat:uzqoqqswl5habowueqa6tcfjnu

LPD-AE: Latent Space Representation of Large-scale 3D Point Cloud

Chuanzhe Suo, Zhe Liu, Lingfei Mo, Yunhui Liu
2020 IEEE Access  
The effective latent space representation of point cloud provides a foremost and fundamental manner that can be used for challenging tasks, including point cloud based place recognition and reconstruction  ...  The encoder network constructs the discriminative global descriptors to realize high accuracy and robust place recognition, which contributed by extracting the local neighbor geometric features and aggregating  ...  SegMap [18] leveraged a data-driven descriptor to extract the feature of voxelized segments in point clouds and performed reconstruction with 3D convolutional layers, which cost the amount of computation  ... 
doi:10.1109/access.2020.2999727 fatcat:i55pbvejize3paprphcfwlki6a

A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks [article]

Baichuan Huang, Jun Zhao, Jingbin Liu
2020 arXiv   pre-print
Simultaneous Localization and Mapping (SLAM) achieves the purpose of simultaneous positioning and map construction based on self-perception.  ...  For Lidar and visual fused SLAM, the paper highlights the multi-sensors calibration, the fusion in hardware, data, task layer.  ...  SegMap: 3d segment mapping using data-driven descriptors. In Robotics: Science and Systems (RSS), 2018. [34] Bichen Wu, Alvin Wan, Xiangyu Yue, and Kurt Keutzer.  ... 
arXiv:1909.05214v4 fatcat:itnluvkewfd6fel7x65wdgig3e

Place recognition survey: An update on deep learning approaches [article]

Tiago Barros, Ricardo Pereira, Luís Garrote, Cristiano Premebida, Urbano J. Nunes
2022 arXiv   pre-print
This paper surveys recent approaches and methods used in place recognition, particularly those based on deep learning.  ...  A key component that enables these intelligent vehicles to overcome such conditions and become more autonomous is the sophistication of the perception and localization systems.  ...  [77] propose SegMap, an data-driven learning approach for the task of localization and mapping.  ... 
arXiv:2106.10458v3 fatcat:bbfv4qympffaphojhxkc4og4am

BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR [article]

Georgi Pramatarov, Daniele De Martini, Matthew Gadd, Paul Newman
2022 arXiv   pre-print
This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching.  ...  This representation is very concise, condensing the size of maps by a factor of 25 against the state-of-the-art, requiring only 3kB to represent a 1.4MB laser scan.  ...  ACKNOWLEDGEMENTS Thanks to the Assuring Autonomy International Programme, a partnership between Lloyd's Register Foundation and the University of York, and EPSRC Programme Grant "From Sensing to Collaboration  ... 
arXiv:2206.15154v1 fatcat:p44tcp4hrbeylmmnwl33mtbufy

Robotics and Autonomous Robots [chapter]

Henry I. Ibekwe, Ali K. Kamrani
2008 Collaborative Engineering  
Siegwart, and C. Cadena. 2018. "SegMap: 3D Segment Mapping using Data-Driven Descriptors". arXiv preprint arXiv:1804.09557. Endres, F., J. Hess, J. Sturm, D. Cremers, and W.  ...  Recent advances in control, onboard sensing and processing, alongside a set of contributions related to the problems of Simultaneous Localization And Mapping (SLAM) and path planning, have paved the way  ... 
doi:10.1007/978-0-387-47321-5_9 fatcat:mhmnwk7ksvgrle7cjgja5et2ke

High-Definition Map Generation Technologies For Autonomous Driving [article]

Zhibin Bao, Sabir Hossain, Haoxiang Lang, Xianke Lin
2022 arXiv   pre-print
This review introduces the concept of HD maps and their usefulness in autonomous driving and gives a detailed overview of HD map generation techniques.  ...  Because of the high precision and informative level of HD maps in localization, it has immediately become one of the critical components of autonomous driving.  ...  Therefore, there is an improvement in localization due to data-driven segment descriptor since it provides less coarse data.  ... 
arXiv:2206.05400v2 fatcat:yj6tq4pl5vbotbckyatlaq7eee

Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving

Sungjin Cho, Chansoo Kim, Jaehyun Park, Myoungho Sunwoo, Kichun Jo
2020 Sensors  
LiDAR-based Simultaneous Localization And Mapping (SLAM), which provides environmental information for autonomous vehicles by map building, is a major challenge for autonomous driving.  ...  The uncertainty is explicitly modeled by proposed probability models which are come from the data-driven approaches.  ...  It incrementally integrates semantic information into a dense 3-D map. Dube et al. proposed SegMap [33] which is segment-based mapping using data-driven descriptors.  ... 
doi:10.3390/s20205900 pmid:33086561 fatcat:udzkihzaungsxcb6gh5lgt2s4u
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