Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-13T05:48:56.164Z Has data issue: false hasContentIssue false
  • English
  • Français

A new feature parametrization for monocular SLAM using line features

Published online by Cambridge University Press:  05 March 2014

Liang Zhao ,
Shoudong Huang ,
Lei Yan  and
Gamini Dissanayake
Liang Zhao*
Affiliation:
Centre for Autonomous Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
Shoudong Huang
Affiliation:
Centre for Autonomous Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
Lei Yan
Affiliation:
Institute of Remote Sensing and GIS, School of Earth and Space Science, Peking University, Beijing, 100871, China
Gamini Dissanayake
Affiliation:
Centre for Autonomous Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
*
*Corresponding author. E-mail: Liang.Zhao-1@uts.edu.au
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a new monocular SLAM algorithm that uses straight lines extracted from images to represent the environment. A line is parametrized by two pairs of azimuth and elevation angles together with the two corresponding camera centres as anchors making the feature initialization relatively straightforward. There is no redundancy in the state vector as this is a minimal representation. A bundle adjustment (BA) algorithm that minimizes the reprojection error of the line features is developed for solving the monocular SLAM problem with only line features. A new map joining algorithm which can automatically optimize the relative scales of the local maps is used to combine the local maps generated using BA. Results from both simulations and experimental datasets are used to demonstrate the accuracy and consistency of the proposed BA and map joining algorithms.

Keywords

SLAMComputer visionLine feature parametrizationBundle adjustmentMap joining
Type
Articles
Information
Robotica , Volume 33 , Issue 3 , March 2015 , pp. 513 - 536
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Davison, A. J., “Real-time Simultaneous Localisation and Mapping with a Single Camera,” Proceedings of the International Conference on Computer Vision (ICCV), vol. 2 (2003) pp. 1403–1410.Google Scholar
2.Klein, G. and Murray, D., “Improving the Agility of Keyframe-based SLAM,” Proceedings of the 10th European Conference on Computer Vision (ECCV), Marseille (2008) pp. 802–815.Google Scholar
3.Sola, J., Calleja, T. V., Civera, J. and Montiel, J. M. M., “Impact of landmark parametrization on monocular EKF-SLAM with points and lines,” Int. J. Comput. Vis. 97 (3), 339–368 (2012).Google Scholar
4.Simon, D. and Chia, T. L., “Kalman filtering with state equality constraints,” IEEE Trans. Aerpspace Electron. Syst. 38 (1), 128–136 (2002).Google Scholar
5.Julier, S. J. and LaViola, J. J., “On Kalman filtering with nonlinear equality constraints,” IEEE Trans. Signal Process. 55 (6), 2774–2784 (2007).Google Scholar
6.Bartoli, A. and Sturm, P., “Structure-from-motion using lines: Representation, triangulation and bundle adjustment,” Comput. Vis. Image Underst. 100 (3), 416–441 (2005).Google Scholar
7.Strasdat, H., Montiel, J. M. M. and Davison, A. J., “Real-time Monocular SLAM: Why Filter?” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Anchorage, USA (May 2010) pp. 2657–2664.Google Scholar
8.Konolige, K. and Agrawal, M., “FrameSLAM: From bundle adjustment to real-time visual mapping,” IEEE Trans. Robot. 24 (5), 1066–1077 (Oct. 2008).Google Scholar
9.Huang, S., Wang, Z. and Dissanayake, G., “Sparse local submap joining filter for building large-scale maps,” IEEE Trans. Robot. 24 (5), 1121–1130 (Oct. 2008).Google Scholar
10.Eade, E. and Drummond, T., “Edge landmarks in monocular SLAM,” Image Vis. Comput. 27, 588–596 (2009).Google Scholar
11.Klein, G. and Murray, D., “Full-3D Edge Tracking with A Particle Filter,” Proceedings of the British Machine Vision Conference (BMVC), Edinburgh, vol. 3 (2006) pp. 1119–1128.Google Scholar
12.Smith, P., Reid, I. and Davison, A. J., “Real-time Monocular SLAM with Straight Lines,” Proceedings of the British Machine Vision Conference (BMVC), Edinburgh, vol. 1 (2006) pp. 17–26.Google Scholar
13.Gee, A. P. and Mayol-Cuevas, W., “Real-Time Model-Based SLAM using Line Segments,” Proceedings of the 2nd International Symposium on Visual Computing, vol. 4292 (Nov. 2006) pp. 354–363.Google Scholar
14.Taylor, C. J. and Kriegman, D. J., “Structure andmotion from line segments in multiple images,” IEEE Trans. Pattern Anal. Mach. Intell. 17 (11), 1021–1032 (1995).Google Scholar
15.Lemaire, T. and Lacroix, S., “Monocular-vision Based SLAM using Line Segments,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Rome, Italy (2007) pp. 2791–2796.Google Scholar
16.Sola, J., Vidal-Calleja, T. and Devy, M., “Undelayed Initialization of Line Segments in Monocular SLAM,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Saint Louis, USA (Oct. 2009) pp. 1553–1558.Google Scholar
17.Vidal-Calleja, T., Berger, C., Sola, J. and Lacroix, S.. “Large scale multiple robot visual mapping with heterogeneous landmarks in semi-structured terrain,” Robot. Auton. Syst. 59, 654–674 (2011).Google Scholar
18.Hartley, R. and Zisserman, A., Multiple View Geometry in Computer Vision, 2nd edn. (Cambridge University Press, Cambridge, UK, 2003).Google Scholar
19.Hu, G., Huang, S. and Dissanayake, G., “3D I-SLSJF: A Consistent Sparse Local Submap Joining Algorithm for Building Large-Scale 3D Maps,” Proceedings of the 48th IEEE Conference on Decision and Control, Shanghai, China (2009) pp. 6040–6045.Google Scholar
20.Zhao, L., Huang, S., Yan, L., Wang, J., Hu, G. and Dissanayake, G., “Large-Scale Monocular SLAM by Local Bundle Adjustment and Map Joining,” Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore (Dec. 2010) pp. 431–436.Google Scholar
21.Zhao, L., Huang, S., Yan, L. and Dissanayake, G., “Parallax Angle Parametrization for Monocular SLAM,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China (May 2011) pp. 3117–3124.Google Scholar
22.Strasdat, H., Montiel, J. M. M. and Davison, A. J., “Scale Drift-Aware Large Scale Monocular SLAM,” Proceedings of the Robotics: Science and Systems Conference (RSS) (2010).Google Scholar
23.Estrada, C., Neira, J. and Tardos, J. D.. “Hierarchical SLAM: Real-time accurate mapping of large environments,” IEEE Trans. Robot. 21 (4), pp. 588–596 (2005).Google Scholar
24.Mouragnon, E., Lhuillier, M., Dhome, M., Dekeyser, F. and Sayd, P., “Generic and real time structure from motion using local bundle adjustment,” Image Vis. Comput. 27 (8), 1178–1193 (2009).Google Scholar
25.Paz, L. M. and Neira, J., “Optimal Local Map Size for EKF-based SLAM,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Beijing, China (Oct. 2006) pp. 9–15.Google Scholar
26.Huang, S. and Dissanayake, G., “Convergence and consistency analysis for extended Kalman filter based SLAM,” IEEE Trans. Robot. 23 (5), 1036–1049 (2007).Google Scholar
27.Huang, S., Wang, Z., Dissanayake, G. and Frese, U., “Iterated D-SLAM map joining: Evaluating its performance in terms of consistency, accuracy and efficiency,” Auton. Robots 27, 409–429 (2009).Google Scholar
28.Blanco, J. L., “Mobile Robot Programming Toolkit (MRPT),” [Online]. Available at: http://www.mrpt.org/node/239/.Google Scholar
29.Kurlbaum, J. and Frese, U., “A Benchmark Data Set for Data Association,” [Online]. Available at: http://www.sfbtr8.uni-bremen.de/reports.htm. Data available: http://radish.sourceforge.net/Google Scholar
30.Kassir, A. and Peynot, T., “Reliable Automatic Camera-Laser Calibration,” Proceedings of Australasian Conference on Robotics and Automation (ACRA), Brisbane, Australia (Dec. 2010).Google Scholar
31.Canny, J.. “A computational approach to edge detection”, IEEE Trans. Pattern Anal. Mach. Intell. 8 (6), 679–98 (Nov. 1986).CrossRefGoogle ScholarPubMed
32.Neubert, P., Protzel, P., Vidal-Calleja, T. and Lacroix, S., “A Fast Visual Line Segment Tracker,” Proceedings of IEEE International Conference on Emerging Technologies and Factory Automation, Hamburg, Germany (Sep. 2008) pp. 353–360.Google Scholar
33.Kovesi, P. D., “MATLAB and Octave Functions for Computer Vision and Image Processing,” Centre for Exploration Targeting, School of Earth and Environment, The University of Western Australia, [Online]. Available at: http://www.csse.uwa.edu.au/~pk/research/matlabfns/.Google Scholar
34.Castellanos, J. A., Montiel, J. M. M., Neira, J. and Tardos, J., “The SPmap: A probabilistic framework for simultaneous localization and map building,” IEEE Trans. Robot. Autom. 15, 948–953 (1999).Google Scholar