Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Topology-Based 3D Reconstruction of Mid-Level Primitives in Man-Made Environments Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

MC2SLAM: Real-Time Inertial Lidar Odometry Using Two-Scan Motion Compensation

verfasst von : Frank Neuhaus, Tilman Koß, Robert Kohnen, Dietrich Paulus

Erschienen in: Pattern Recognition

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

We propose a real-time, low-drift laser odometry approach that tightly integrates sequentially measured 3D multi-beam LIDAR data with inertial measurements. The laser measurements are motion-compensated using a novel algorithm based on non-rigid registration of two consecutive laser sweeps and a local map. IMU data is being tightly integrated by means of factor-graph optimization on a pose graph. We evaluate our method on a public dataset and also obtain results on our own datasets that contain information not commonly found in existing datasets. At the time of writing, our method was ranked within the top five laser-only algorithms of the KITTI odometry benchmark.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel View-Aware Person Re-identification
Nächstes Kapitel An Analysis by Synthesis Approach for Automatic Vertebral Shape Identification in Clinical QCT
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
1
Our implementation is also able to close loops, but to maintain focus of this work, we decided to focus on the odometry part of the SLAM problem, whose accuracy is essential to obtain accurate maps—even when loops are being closed.
 
2
At the time of writing, the best method using only laser data was the LOAM method by Zhang and Singh. However, the results reported on the KITTI benchmark webpage are no longer equivalent to those reported in their paper [22], indicating that their solution has been updated. The updated algorithm is no longer publicly available.
 
3
This is relatively efficient, because it is done for the set of query points and not for the whole point cloud.
 
4
Note that we use a log map of \(S^3\) to \(\mathbb {R}^3\) for measuring the ‘difference’ between the orientations [11].
 
7
This number was manually determined to be sufficient to reduce the deviation of the results to negligible values.
 
Literatur
1.
2.
Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, vol. 1611, pp. 586–607. International Society for Optics and Photonics (1992)
3.
Board, I.: IEEE standard specification format guide and test procedure for single-axis interferometric fiber optic gyros. IEEE Std., pp. 952–1997 (1998)
4.
Bosse, M., Zlot, R., Flick, P.: Zebedee: design of a spring-mounted 3-D range sensor with application to mobile mapping. IEEE Trans. Robot. 28(5), 1104–1119 (2012)CrossRef
5.
Cvišić, I., Petrović, I.: Stereo odometry based on careful feature selection and tracking. In: 2015 European Conference on Mobile Robots, ECMR, pp. 1–6. IEEE (2015)
6.
7.
8.
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition, CVPR (2012)
9.
Hertzberg, C., Wagner, R., Frese, U., Schröder, L.: Integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds. Inf. Fusion 14(1), 57–77 (2013)CrossRef
10.
Hesch, J.A., Kottas, D.G., Bowman, S.L., Roumeliotis, S.I.: Consistency analysis and improvement of vision-aided inertial navigation. IEEE Trans. Robot. 30(1), 158–176 (2014)CrossRef
11.
Kaess, M.: Simultaneous localization and mapping with infinite planes. In: ICRA, vol. 1, p. 2 (2015)
12.
Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J.J., Dellaert, F.: iSAM2: Incremental smoothing and mapping using the Bayes tree. Int. J. Robot. Res. 31(2), 216–235 (2012)CrossRef
13.
Kukko, A., Kaartinen, H., Hyyppä, J., Chen, Y.: Multiplatform mobile laser scanning: usability and performance. Sensors 12(9), 11712–11733 (2012)CrossRef
14.
Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., Furgale, P.: Keyframe-based visual–inertial odometry using nonlinear optimization. Int. J. Robot. Res. 34(3), 314–334 (2015)CrossRef
15.
Low, K.L.: Linear least-squares optimization for point-to-plane ICP surface registration, no. 4. Chapel Hill, University of North Carolina (2004)
16.
Moosmann, F., Stiller, C.: Velodyne SLAM. In: Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 393–398, Baden-Baden, Germany, June 2011
17.
Nüchter, A., Bleier, M., Schauer, J., Janotta, P.: Improving Google’s cartographer 3D mapping by continuous-time SLAM. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 42, 543 (2017)CrossRef
18.
19.
Rönnholm, P., Liang, X., Kukko, A., Jaakkola, A., Hyyppä, J.: Quality analysis and correction of mobile backpack laser scanning data. ISPRS Ann. Photogram. Remote Sens. Spat. Inf. Sci. 3, 41 (2016)CrossRef
20.
Zhang, J., Kaess, M., Singh, S.: Real-time depth enhanced monocular odometry. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014, pp. 4973–4980. IEEE (2014)
21.
Zhang, J., Kaess, M., Singh, S.: A real-time method for depth enhanced visual odometry. Auton. Robots 41(1), 31–43 (2017)CrossRef
22.
Zhang, J., Singh, S.: LOAM: lidar odometry and mapping in real-time. In: Robotics: Science and Systems, vol. 2 (2014)
23.
Zhang, J., Singh, S.: Visual-lidar odometry and mapping: low-drift, robust, and fast. In: 2015 IEEE International Conference on Robotics and Automation, ICRA, pp. 2174–2181. IEEE (2015)
24.
Zhang, J., Singh, S.: Low-drift and real-time lidar odometry and mapping. Auton. Robots 41(2), 401–416 (2017)CrossRef
Metadaten
Titel
MC2SLAM: Real-Time Inertial Lidar Odometry Using Two-Scan Motion Compensation
verfasst von
Frank Neuhaus
Tilman Koß
Robert Kohnen
Dietrich Paulus
Copyright-Jahr
2019
Buch
Pattern Recognition

Print ISBN: 978-3-030-12938-5

Electronic ISBN: 978-3-030-12939-2

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-12939-2_5