Full Text:   <2822>

Summary:  <2020>

CLC number: TP391

On-line Access: 2015-01-29

Received: 2014-04-20

Revision Accepted: 2014-11-24

Crosschecked: 2015-01-06

Cited: 2

Clicked: 7032

Citations:  Bibtex RefMan EndNote GB/T7714


Wei Lu


Zhi-yu Xiang


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.2 P.152-165


Design of an enhanced visual odometry by building and matching compressive panoramic landmarks online

Author(s):  Wei Lu, Zhi-yu Xiang, Ji-lin Liu

Affiliation(s):  Institute of Information and Communication Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   lwhfh01@zju.edu.cn, xiangzy@zju.edu.cn

Key Words:  Visual odometry, Panoramic landmark, Landmark matching, Compressed sensing, Adaptive compressive feature

Wei Lu, Zhi-yu Xiang, Ji-lin Liu. Design of an enhanced visual odometry by building and matching compressive panoramic landmarks online[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(2): 152-165.

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Efficient and precise localization is a prerequisite for the intelligent navigation of mobile robots. Traditional visual localization systems, such as visual odometry (VO) and simultaneous localization and mapping (SLAM), suffer from two shortcomings: a drift problem caused by accumulated localization error, and erroneous motion estimation due to illumination variation and moving objects. In this paper, we propose an enhanced VO by introducing a panoramic camera into the traditional stereo-only VO system. Benefiting from the 360° field of view, the panoramic camera is responsible for three tasks: (1) detecting road junctions and building a landmark library online; (2) correcting the robot’s position when the landmarks are revisited with any orientation; (3) working as a panoramic compass when the stereo VO cannot provide reliable positioning results. To use the large-sized panoramic images efficiently, the concept of compressed sensing is introduced into the solution and an adaptive compressive feature is presented. Combined with our previous two-stage local binocular bundle adjustment (TLBBA) stereo VO, the new system can obtain reliable positioning results in quasi-real time. Experimental results of challenging long-range tests show that our enhanced VO is much more accurate and robust than the traditional VO, thanks to the compressive panoramic landmarks built online.

The paper presents a method for enhancing the visual odometry using two kinds of vision sensors, like stereo cameras and panoramic (360 degree-omnidirectional) camera. The proposed method aims to reduce the error of motion estimation based on matching the landmarks in panoramic images with database in the library as the robot is revisiting the scenes, which focuses on special scenes such as road junctions and buildings. Authors utilize the panoramic camera for estimating the azimuthal rotation of robot when the stereo VO (visual odometry) could not sufficiently provide reliable position estimation results. The experimental results demonstrate the efficiency and effectiveness of the proposed method under varied conditions. It would be a good idea to compare the result with other landmark matching localization schemes. This comparison would be more useful if the authors are able to quantify the benefit (may be in terms of frequency and accuracy) of the proposed work. The repeatability of the proposed scheme should also be commented. In general, the paper is fairly well written. Overall quality of the research is acceptable.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Bay, H., Tuytelaars, T., van Gool, L., 2006. SURF: speeded up robust features. Proc. 9th European Conf. on Computer Vision, p.404-417.

[2]Cai, X., Zhang, Z., Zhang, H., et al., 2014. Soft consistency reconstruction: a robust 1-bit compressive sensing algorithm. arXiv:1402.5475 (preprint).

[3]Candes, E.J., Tao, T., 2005. Decoding by linear programming. IEEE Trans. Inform. Theory, 51(12):4203-4215.

[4]Donoho, D.L., 2006. Compressed sensing. IEEE Trans. Inform. Theory, 52(4):1289-1306.

[5]Durrant-Whyte, H., Bailey, T., 2006. Simultaneous localization and mapping: part I. IEEE Robot. Autom. Mag., 13(2):99-110.

[6]Fischler, M.A., Bolles, R.C., 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6):381-395.

[7]Fraundorfer, F., Scaramuzza, D., 2012. Visual odometry: part II. Matching, robustness, optimization, and applications. IEEE Robot. Autom. Mag., 19(2):78-90.

[8]Galvez-López, D., Tardos, J.D., 2012. Bags of binary words for fast place recognition in image sequences. IEEE Trans. Robot., 28(5):1188-1197.

[9]Geiger, A., Lenz, P., Urtasun, R., 2012. Are we ready for autonomous driving? The KITTI vision benchmark suite. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.3354-3361.

[10]Horn, B.K.P., 1987. Closed-form solution of absolute orientation using unit quaternions. JOSA A, 4(4):629-642.

[11]Konolige, K., Agrawal, M., Solà, J., 2011. Large-scale visual odometry for rough terrain. Proc. 13th Int. Symp. on Robotics Research, p.201-212.

[12]Liu, Y., Zhang, H., 2012. Visual loop closure detection with a compact image descriptor. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.1051-1056.

[13]Lu, W., Xiang, Z., Liu, J., 2013. High-performance visual odometry with two-stage local binocular BA and GPU. Proc. IEEE Intelligent Vehicles Symp., p.1107-1112.

[14]Munguia, R., Grau, A., 2007. Monocular SLAM for visual odometry. Proc. IEEE Int. Symp. on Intelligent Signal Processing, p.1-6.

[15]Nistér, D., Naroditsky, O., Bergen, J., 2004. Visual odometry. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.652-659.

[16]Scaramuzza, D., Fraundorfer, F., 2011. Visual odometry (tutorial). IEEE Robot. Autom. Mag., 18(4):80-92.

[17]Se, S., Lowe, D., Little, J., 2002. Global localization using distinctive visual features. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.226-231.

[18]Singh, G., Košechá, J., 2010. Visual loop closing using gist descriptors in Manhattan world. ICRA Omnidirectional Vision Workshop.

[19]Sivic, J., Zisserman, A., 2003. Video Google: a text retrieval approach to object matching in videos. Proc. 9th IEEE Int. Conf. on Computer Vision, p.1470-1477.

[20]Stewénius, H., Engels, C., Nistér, D., 2006. Recent developments on direct relative orientation. ISPRS J. Photogr. Remote Sens., 60(4):284-294.

[21]Sünderhauf, N., Protzel, P., 2011. BRIEF-Gist—closing the loop by simple means. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.1234-1241.

[22]Wang, Y., 2013. Navigational Road Modeling Based on Omnidirectional Multi-camera System. PhD Thesis, Zhejiang University, Hangzhou, China (in Chinese).

[23]Wright, J., Yang, A.Y., Ganesh, A., et al., 2009. Robust face recognition via sparse representation. IEEE Trans. Patt. Anal. Mach. Intell., 31(2):210-227.

[24]Wu, C., 2007. SiftGPU: a GPU Implementation of Scale Invariant Feature Transform (SIFT). Available from http://cs.unc.edu/~ccwu/siftgpu/.

[25]Zhang, K., Zhang, L., Yang, M.H., 2012. Real-time compressive tracking. Proc. 12th European Conf. on Computer Vision, p.864-877.

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