NOSeqSLAM: Not only Sequential SLAM

  • Jurica MaltarEmail author
  • Ivan Marković
  • Ivan Petrović
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1092)


The essential property that every autonomous system should have is the ability to localize itself, i.e., to reason about its location relative to measured landmarks and leverage this information to consistently estimate vehicle location through time. One approach to solving the localization problem is visual place recognition. Using only camera images, this approach has the following goal: during the second traversal through the environment, using only current images, find the match in the database that was created during a previously driven traversal of the same route. Besides the image representation method – in this paper we use feature maps extracted from the OverFeat architecture – for visual place recognition it is also paramount to perform the scene matching in a proper way. For autonomous vehicles and robots traversing through an environment, images are acquired sequentially, thus visual place recognition localization approaches use the structure of sequentiality to locally match image sequences to the database for higher accuracy. In this paper we propose a not only sequential approach to localization; specifically, instead of linearly searching for sequences, we construct a directed acyclic graph and search for any kind of sequences. We evaluated the proposed approach on a dataset consisting of varying environmental conditions and demonstrated that it outperforms the SeqSLAM approach.


Visual place recognition Localization SeqSLAM Deep convolutional neural networks 


  1. 1.
    Arandjelović, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: NetVLAD: CNN architecture for weakly supervised place recognition 70(5), 641–648 (2015)Google Scholar
  2. 2.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  3. 3.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2010)Google Scholar
  4. 4.
    Chen, Z., Jacobson, A., Sünderhauf, N., Upcroft, B., Liu, L., Shen, C., Reid, I., Milford, M.: Deep learning features at scale for visual place recognition. In: Proceedings - IEEE International Conference on Robotics and Automation, vol. 1, pp. 3223–3230 (2017)Google Scholar
  5. 5.
    Cummins, M., Newman, P.: FAB-MAP: probabilistic localization and mapping in the space of appearance. Int. J. Robot. Res. 27(6), 647–665 (2008)CrossRefGoogle Scholar
  6. 6.
    Garg, S., Babu, M., Dharmasiri, T., Hausler, S., Sünderhauf, N., Kumar, S., Drummond, T., Milford, M.: Look no deeper: recognizing places from opposing viewpoints under varying scene appearance using single-view depth estimation (2019)Google Scholar
  7. 7.
    Garg, S., Sünderhauf, N., Milford, M.: LoST? Appearance-invariant place recognition for opposite viewpoints using visual semantics (2018)Google Scholar
  8. 8.
    Hausler, S., Jacobson, A., Milford, M.: Feature map filtering: improving visual place recognition with convolutional calibration (2018)Google Scholar
  9. 9.
    Jegou, H., Douze, M., Schmid, C., Perez, P.: Aggregating local descriptors into a compact image representation. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3304–3311. IEEE (2010)Google Scholar
  10. 10.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)Google Scholar
  11. 11.
    Lowry, S., Milford, M.J.: Supervised and unsupervised linear learning techniques for visual place recognition in changing environments. IEEE Trans. Robot. 32(3), 600–613 (2016)CrossRefGoogle Scholar
  12. 12.
    Milford, M.J., Wyeth, G.F.: SeqSLAM: visual route-based navigation for sunny summer days and stormy winter nights. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 1643–1649. IEEE (2012)Google Scholar
  13. 13.
    Mur-Artal, R., Montiel, J.M.M., Tardós, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. CoRR abs/1502.0 (2015)Google Scholar
  14. 14.
    Naseer, T., Burgard, W., Stachniss, C.: Robust visual localization across seasons. IEEE Trans. Robot. 34(2), 289–302 (2018)CrossRefGoogle Scholar
  15. 15.
    Naseer, T., Ruhnke, M., Stachniss, C., Spinello, L., Burgard, W.: Robust visual SLAM across seasons. In: IEEE International Conference on Intelligent Robots and Systems, December 2015, pp. 2529–2535 (2015)Google Scholar
  16. 16.
    Naseer, T., Spinello, L., Burgard, W., Stachniss, C.: Robust visual robot localization across seasons using network flows. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2564–2570 (2014)Google Scholar
  17. 17.
    Pepperell, E., Corke, P.I., Milford, M.J.: All-environment visual place recognition with smart. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 1612–1618 (2014)Google Scholar
  18. 18.
    Razavian, A.S., Sullivan, J., Carlsson, S., Maki, A.: Visual Instance Retrieval with Deep Convolutional Networks (June 2017) (2014)Google Scholar
  19. 19.
    Rosten, E., Porter, R., Drummond, T.: Faster and better: a machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 105–119 (2010)CrossRefGoogle Scholar
  20. 20.
    Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., Lecun, Y.: OverFeat: integrated recognition, localization and detection using convolutional networks. In: International Conference on Learning Representations (ICLR2014), CBLS, April 2014 (2014)Google Scholar
  21. 21.
    Siam, S.M., Zhang, H.: Fast-SeqSLAM: a fast appearance based place recognition algorithm. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5702–5708 (2017)Google Scholar
  22. 22.
    Sünderhauf, N., Shirazi, S., Dayoub, F., Upcroft, B., Milford, M.: On the performance of ConvNet features for place recognition. In: IEEE International Conference on Intelligent Robots and Systems, December 2015, pp. 4297–4304 (2015)Google Scholar
  23. 23.
    Sünderhauf, N., Shirazi, S., Jacobson, A., Dayoub, F., Pepperell, E., Upcroft, B., Milford, M.: Place recognition with ConvNet landmarks: viewpoint-robust, condition-robust, training-free. In: Robotics: Science and Systems XI, Robotics: Science and Systems Foundation (2015)Google Scholar
  24. 24.
    Talbot, B., Garg, S., Milford, M.: OpenSeqSLAM2.0: an open source toolbox for visual place recognition under changing conditions. IEEE Robot. Autom. Lett. 1(1), 213–220 (2018)Google Scholar
  25. 25.
    Vysotska, O., Naseer, T., Spinello, L., Burgard, W., Stachniss, C.: Efficient and effective matching of image sequences under substantial appearance changes exploiting GPS priors. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 2774–2779 (2015)Google Scholar
  26. 26.
    Vysotska, O., Stachniss, C.: Lazy data association for image sequences matching under substantial appearance changes. IEEE Robot. Autom. Lett. 1(1), 213–220 (2016)CrossRefGoogle Scholar
  27. 27.
    Yin, P., Srivatsan, R.A., Chen, Y., Li, X., Zhang, H., Xu, L., Li, L., Jia, Z., Ji, J., He, Y.: MRS-VPR: a multi-resolution sampling based global visual place recognition method (2019)Google Scholar
  28. 28.
    Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40, 1452–1464 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of OsijekOsijekCroatia
  2. 2.Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia

Personalised recommendations