Visual navigation method for indoor mobile robot based on extended BoW model

Xianghui Li; Xinde Li; Mohammad Omar Khyam; Chaomin Luo; Yingzi Tan

Visual navigation method for indoor mobile robot based on extended BoW model

View Fulltext

Author(s): Xianghui Li¹ ; Xinde Li^{1, 4} ; Mohammad Omar Khyam² ; Chaomin Luo³ ; Yingzi Tan¹
- Affiliations: 1: Key Laboratory of Measurement and Control of CSE (Ministry of Education), School of Automation, Southeast University , Nanjing 210096 , People's Republic of China ;
  2: Department of Electrical and Computer Engineering, Faculty of Engineering, National University of Singapore , 117580 , Singapore ;
  3: Department of Electrical and Computer Engineering, University of Detroit Mercy , Michigan , USA ;
  4: School of Cyber Science and Engineering, Southeast University , Nanjing, 211189 , People's Republic of China
Source: Volume 2, Issue 4, December 2017, p. 142 – 147
DOI: 10.1049/trit.2017.0020 , Online ISSN 2468-2322

This is an open access article published by the IET, Chinese Association for Artificial Intelligence and Chongqing University of Technology under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

Received 08/09/2017, Accepted 10/11/2017, Revised 02/11/2017, Published 21/03/2019

This article proposes a new navigation method for mobile robots based on an extended bag of words (BoW) model for general object recognition in indoor environments. The scale-invariant feature transform (SIFT)-detection algorithm with the graphic processing unit (GPU) acceleration technology is used to describe feature vectors in this model. First, in order to add some redundant image information, statistical information of the spatial relationships of all the feature points in an image, i.e. relative distances and angles, is used to extend the feature vectors in the original BoW model. Then, the support vector machine (SVM) classifier is used to classify objects. Also, in order to navigate conveniently in unknown and dynamic indoor environments, a type of human–robot interaction method based on a hand-drawn semantic map is considered. The experimental results show that this new navigation technology for indoor mobile robots is very robust and highly effective.

References

1. 1)
  - 20. Lowe, D.G.: ‘Distinctive image features from scale-invariant keypoints’, Int. J. Comput. Vis., 2004, 60, pp. 91–110 (doi: 10.1023/B:VISI.0000029664.99615.94).
2. 2)
  - [23]. Hartigan, J.A.: ‘Clustering algorithms’ (John Wiley and Sons, Inc., New York, NY, USA, 1975).
3. 3)
  - [15]. Cheng, X., Wang, J., Chia, L., et al: ‘Learning to combine multi-resolution spatially-weighted co-occurrence matrices for image representation’. 2010 IEEE Int. Conf. on Multimedia and Expo (ICME), Suntec City, Singapore, 2010, pp. 631–636.
4. 4)
  - [13]. Agarwal, S., Awan, A., Roth, D.: ‘Learning to detect objects in images via a sparse, part-based representation’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (11), pp. 1475–1490 (doi: 10.1109/TPAMI.2004.108).
5. 5)
  - [14]. Liu, X.L., Lou, Y.H., Yu, A.W., et al: ‘Search by mobile image based on visual and spatial consistency’. 2011 IEEE Int. Conf. on Multimedia and Expo (ICME), Barcelona, Spain, 2011, pp. 1–6.
6. 6)
  - [9]. Skubic, M., Bailey, C., Chronis, G.: ‘A sketch interface for mobile robots’. Proc. of the IEEE Int. Conf. on Systems, Man and Cybernetics (SMC), Washington DC, USA, 2003, vol. 1, pp. 919–924.
7. 7)
  - [4]. Wu, X.: ‘Visual navigation method research for mobile based on hand-drawn map’ (Nanjing Southeast University, Nanjing, 2011).
8. 8)
  - [21]. David, G.: ‘Object recognition from local scale-invariant feature’. Proc. of the Seventh IEEE Int. Conf. on Computer Vision, Kerkyra, Greece, 1999, pp. 1150–1157.
9. 9)
  - [5]. Li, X., Zhang, X., Dai, X.: ‘An interactive visual navigation method using a hand-drawn-route-map in an unknown dynamic environment’, Int. J. Fuzzy Syst., 2011, 13, (4), pp. 311–322.
10. 10)
  - 62. Li, T., Mei, T., Kweon, I.-S., Hua, X.-S.: ‘Contextual bag-of-words for visual categorization’, IEEE Trans. Circuits Syst. Video Technol., 2011, 21, (4), pp. 381–392 (doi: 10.1109/TCSVT.2010.2041828).
11. 11)
  - [18]. Lazebnik, S., Schmid, C., Ponce, J.: ‘Beyond bags of features: spatial pyramid matching for recognizing natural scene cat- egories’. Proc. of the IEEE Computer Vision and Pattern Recognition, New York, USA, 2006, pp. 2169–2178.
12. 12)
  - [19]. Zhang, L., Wang, C., Xiao, B., et al: ‘Image representation using bag-of-phrases’, Acta Autom. Sin., 2012, 38, (1), pp. 46–54 (doi: 10.3724/SP.J.1004.2012.00046).
13. 13)
  - [20]. Chen, H.: ‘Research on object classifier based on distinguished learning’ (University of science and technology of China, He Fei, China, 2009).
14. 14)
  - [6]. Kawamura, K., Peters, R.A.I., Wilkes, D.M., et al: ‘Toward perception-based navigation using EgoSphere’. Proc. for SPIE 4573, Mobile Robots XVI, Boston, MA, United States, October 2001.
15. 15)
  - [2]. Se, S., Lowe, D.: ‘Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks’, Int. J. Rob. Res., 2002, 21, (8), pp. 735–738 (doi: 10.1177/027836402761412467).
16. 16)
  - [3]. Shamos, M.I., Hoey, D.: ‘Closest-point problems’. 16th Annual Symp. on Foundations of Computer Science, Berkeley, USA, 1975, pp. 151–162.
17. 17)
  - [24]. Sivic, J., Zisserman, A.: ‘Video google: a text retrieval approach to object matching in video’. Proc. of IEEE Int. Conf. on Computer Vision, Nice, France, 2003, vol. 2, pp. 1470–1477.
18. 18)
  - [7]. Kawamura, K., Koku, A.B., Wilkes, D.M., et al: ‘Toward egocentric navigation’, Int. J. Rob. Autom., 2002, 17, (4), pp. 135–145.
19. 19)
  - [8]. Chronis, G., Skubic, M.: ‘Sketch-based navigation for mobile robots’. Proc. of the 12th IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), St. Louis, USA, 2003, vol. 1, pp. 284–289.
20. 20)
  - [10]. Skubic, M., Blisard, S., Bailey, C., et al: ‘Qualitative analysis of sketched route maps: translating a sketch into linguistic descriptions’, IEEE Trans. Syst. Man Cybern., 2004, 34, (2), pp. 1275–1282 (doi: 10.1109/TSMCB.2003.817099).
21. 21)
  - [1]. Thrun, S.: ‘Robotic mapping: a survey’. CMU-CS-02-111, School of Computer Science, Carnegie Meiion University, Pittsburgh, 2002.
22. 22)
  - [16]. Sun, M., Hamme, V.: ‘Image pattern discovery by using the spatial closeness of visual code words’. 2011 18th IEEE Int. Conf. on Image Processing, Brussels, Belgium, 2011, pp. 205–208.
23. 23)
  - [17]. Elsayad, I., Martinet, J., Urruty, T., et al: ‘A new spatial weighting scheme for bag-of-visual-words’. 2010 Int. Workshop on Content-Based Multimedia Indexing, Grenoble, France, 2010, pp. 1–6.
24. 24)
  - [12]. Liu, T., Liu, J., Liu, Q.S., et al: ‘Expanded bag of words representation for object classification’. Processing of 2009 16th IEEE Int. Conf. on Image, Cairo, Egypt, 2009, pp. 297–300.

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Visual navigation method for indoor mobile robot based on extended BoW model

References

Related content