Skip to main content Accessibility help
×
Home

Topological simultaneous localization and mapping: a survey

  • Jaime Boal (a1), Álvaro Sánchez-Miralles (a1) and Álvaro Arranz (a1)

Summary

One of the main challenges in robotics is navigating autonomously through large, unknown, and unstructured environments. Simultaneous localization and mapping (SLAM) is currently regarded as a viable solution for this problem. As the traditional metric approach to SLAM is experiencing computational difficulties when exploring large areas, increasing attention is being paid to topological SLAM, which is bound to provide sufficiently accurate location estimates, while being significantly less computationally demanding. This paper intends to provide an introductory overview of the most prominent techniques that have been applied to topological SLAM in terms of feature detection, map matching, and map fusion.

Copyright

Corresponding author

*Corresponding author. E-mail: jaime.boal@iit.upcomillas.es

References

Hide All
1.Agrawal, M., Konolige, K. and Blas, M. R., “CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching,” LNCN 5305, 102115 (2008).
2.Aguilar, W., Frauel, Y., Escolano, F., Martínez-Pérez, M. E., Espinosa-Romero, A. and Lozano, M. Á., “A robust graph transformation matching for non-rigid registration,” Image Vis. Comput. 27, 897910 (2009).
3.Andreasson, H., Treptow, A. and Duckett, T., “Localization for Mobile Robots Using Panoramic Vision, Local Features and Particle Filter,” Proceedings of the IEEE International Conference Robotics and Automation, Barcelona, Spain (2005) pp. 33483353.
4.Angeli, A., Doncieux, S., Meyer, J.-A. and Filliat, D., “Incremental Vision-Based Topological SLAM,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (2008a) pp. 10311036.
5.Angeli, A., Filliat, D., Doncieux, S. and Meyer, J.-A., “A fast and incremental method for loop-closure detection using bags of visual words,” IEEE Trans. Robot. 24 (5), 10271037 (2008b).
6.Angeli, A., Filliat, D., Doncieux, S. and Meyer, J.-A., “Real-Time Visual Loop-Closure Detection,” Proceedings of the IEEE International Conference on Robotics and Automation, Pasadena, CA (2008c) pp. 18421847.
7.Anguelov, D., Koller, D., Parker, E. and Thrun, S., “Detecting and modeling doors with mobile robots,” Proc. IEEE Int. Conf. Robot. Autom. 4, 37773784 (2004).
8.Bailey, T. and Durrant-Whyte, H. F., “Simultaneous localization and mapping (SLAM): Part II,” IEEE Robot. Autom. Mag. 13 (3), 108117 (2006).
9.Bay, H., Ess, A., Tuytelaars, T. and van Gool, L., “SURF: Speeded up robust features,” Comput. Vis. Image Underst. 110 (3), 346359 (2008).
10.Beeson, P., Jong, N. K. and Kuipers, B., “Towards Autonomous Topological Place Detection Using the Extended Voronoi Graph,” Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain (2005) pp. 43734379.
11.Blanco, J. L., Fernández-Madrigal, J. A. and González, J., “Toward a unified Bayesian approach to hybrid metric-topological SLAM,” IEEE Trans. Robot. 24 (2), 259270 (2008).
12.Blanco, J. L., González-Jiménez, J. and Fernández-Madrigal, J. A., “Sparser Relative Bundle Adjustment (SRBA): Constant-Time Maintenance and Local Optimization of Arbitrarily Large Maps,” Proceedings of the IEEE International Conference Robotics and Automation, Karlsruhe, Germany (2013) pp. 7077.
13.Bradski, G., “The OpenCV library” (2000). http://opencv.willowgarage.com/.
14.Brooks, R. A., “Visual map making for a mobile robot,” Proc. IEEE Int. Conf. Robot. Autom. 2, 824829 (1985).
15.Brooks, R. A., “Elephants don't play chess,” Robot. Auton. Syst. 6, 315 (1990).
16.Cassandra, A. R., Kaelbling, L. P. and Kurien, J. A., “Acting Under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 2, Osaka, Japan (1996) pp. 963972.
17.Chatila, R. and Laumond, J.-P., “Position referencing and consistent world modeling for mobile robots,” Proc. IEEE Int. Conf. Robot. Autom. 2, 138145 (1985).
18.Choset, H. and Nagatani, K., “Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization,” IEEE Trans. Robot. Autom. 17 (2), 125137 (2001).
19.Csurka, G., Dance, C., Fan, L., Williamowski, J. and Bray, C., “Visual Categorization with Bags of Keypoints,” ECCV International Workshop on Statistical Learning in Computer Vision, Prague, Czech Republic (2004) pp. 5974.
20.Cummins, M., Probabilistic Localization and Mapping in Appearance Space Ph.D. Thesis (University of Oxford, 2009).
21.Cummins, M. and Newman, P., “Probabilistic Appearance Based Navigation and Loop Closing,” Proceedings of the IEEE International Conference on Robotics and Automation, Rome, Italy (2007) pp. 20422048.
22.Cummins, M. and Newman, P., “FAB-MAP: Probabilistic localization and mapping in the space of appearance,” Int. J. Robot. Res. 27 (6), 647665 (2008).
23.Cummins, M. and Newman, P., “Accelerating FAB-MAP with concentration inequalities,” IEEE Trans. Robot. 26 (6), 10421050 (2010a).
24.Cummins, M. and Newman, P., “FAB-MAP: Appearance-Based Place Recognition and Mapping Using a Learned Visual Vocabulary Model,” Proceedings of the International Conference on Machine Learning, Haifa, Israel (2010b) pp. 310.
25.Cummins, M. and Newman, P., “Appearance-only SLAM at large scale with FAB-MAP 2.0,” Int. J. Robot. Res. 30 (9), 11001123 (2011).
26.Dempster, A. P., “Upper and lower probabilities induced by a multivalued mapping,” Ann. Math. Stat. 38 (2), 325339 (1967).
27.Deng, Y. and Manjunath, B. S., “Unsupervised segmentation of color-texture regions in images and video,” IEEE Trans. Pattern Anal. Mach. Intell. 23 (8), 800810 (2001).
28.Doh, N. L., Lee, K., Chung, W. K. and Cho, H., “Simultaneous localisation and mapping algorithm for topological maps with dynamics,” IET Control Theor. Appl. 3 (9), 12491260 (2009).
29.Doucet, A., de Freitas, N., Murphy, K. and Russell, S., “Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks,” Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, Stanford, CA (2000a) pp. 176–173.
30.Doucet, A., Godsill, S. and Andrieu, C., “On sequential Monte Carlo sampling methods for Bayesian filtering,” Stat. Comput. 10, 197208 (2000b).
31.Duckett, T., Marsland, S. and Shapiro, J., “Learning Globally Consistent Maps by Relaxation,” Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA, USA (2000) pp. 38413846.
32.Dudek, G., Freedman, P. and Hadjres, S., “Using Local Information in a Non-Local Way for Mapping Graph-Like Worlds,” International Joint Conference on Artificial Intelligence, Chambery, France (1993) pp. 16391647.
33.Durrant-Whyte, H. F. and Bailey, T., “Simultaneous localization and mapping: Part I,” IEEE Robot. Autom. Mag. 13 (2), 99110 (2006).
34.Ebrahimi, M. and Mayol-Cuevas, W. W., “SUSurE: Speeded up Surround Extrema Feature Detector and Descriptor for Realtime Applications,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, FL (2009) pp. 914.
35.Filliat, D., “A Visual Bag of Words Method for Interactive Qualitative Localization and Mapping,” Proceedings of the IEEE International Conference on Robotics and Automation, Rome, Italy (2007) pp. 39213926.
36.Filliat, D. and Meyer, J.-A., “Map-based navigation in mobile robots: I. A review of localization strategies,” Cogn. Syst. Res. 4 (4), 243282 (2003).
37.Fischler, M. A. and Bolles, R. C., “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24 (6), 381395 (1981).
38.Forsyth, D. A. and Ponce, J., Computer Vision: A Modern Approach (Prentice Hall, Upper Saddle River, NJ, 2003).
39.Fraundorfer, F., Engels, C. and Nistér, D., “Topological Mapping, Localization and Navigation Using Image Collections,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA (2007) pp. 38723877.
40.Goedemé, T. and van Gool, L., “Robust Vision-Only Mobile Robot Navigation with Topological Maps”, In: Mobile Robots Motion Planning, New Challenges (Jing, X.-J., ed.) (InTech, Austria, 2008) chap. 4, pp. 6388.
41.Goedemé, T., Nuttin, M., Tuytelaars, T. and van Gool, L., “Omnidirectional vision based topological navigation,” Int. J. Comput. Vis. 74 (3), 219236 (2007).
42.Goedemé, T., Tuytelaars, T. and van Gool, L., “Fast wide baseline matching for visual navigation,” Proc. IEEE Comp. Soc. Conf. Comp. Vision Pattern Recogn. 1, 2429 (2004).
43.Goedemé, T., Tuytelaars, T., van Gool, L., Vanacker, G. and Nuttin, M., “Feature Based Omnidirectional Sparse Visual Path Following,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada (2005) pp. 18061811.
44.Gutiérrez-Osuna, R. and Luo, R. C., “LOLA Probabilistic navigation for topological maps,” AI Mag. 17 (1), 5562 (1996).
45.Gutmann, J.-S. and Konolige, K., “Incremental Mapping of Large Cyclic Environments”, Proceedings of the International Symposium on Computational Intelligence in Robotics and Automation (1999), pp. 318–325.
46.Hafner, H. H., “Learning Places in Newly Explored Environments,” In: Proceedings of the International Conference on Simulation of Adaptive Behavior (Meyer, Berthoz, Floreano, Roitblat and Wilson, eds.) (International Society for Adaptive Behavior, Honolulu, HI, USA, 2000) pp. 111120.
47.Ho, N. and Jarvis, R., “Vision Based Global Localisation Using a 3D Environmental Model Created by a Laser Range Scanner,” Proceedings of the IEEE/RSJ International Conference Intelligent Robots and Systems, Nice, France (2008) pp. 29642969.
48.Itti, L. and Baldi, P., “A principled approach to detecting surprising events in video,” Proc. IEEE Comp. Soc. Conf. Comp. Vision Pattern Recogn. 1, 631637 (2005).
49.Jacobs, C. E., Finkelstein, A. and Salesin, D. H., “Fast Multiresolution Image Querying,” Proceedings of the Annual Conference on Computer Graphics and Interactive Techniques, Los Angeles, CA (1995) pp. 277286.
50.Johnson, C. and Kuipers, B., “Efficient Search for Correct and Useful Topological Maps,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Algarve, Portugal (2012) pp. 52775282.
51.Kaelbling, L. P., Littman, M. L. and Cassandra, A. R., “Planning and acting in partially observable stochastic domains,” Artif. Intell. 101 (1–2), 99134 (1998).
52.Koenig, A., Kessler, J. and Gross, H.-M., “A Graph Matching Technique for an Appearance-Based, Visual SLAM-Approach Using Rao-Blackwellized Particle Filters,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (2008) pp. 15761581.
53.Koenig, S., Mudgal, A. and Tovey, C., “A Near-Tight Approximation Lower Bound and Algorithm for the Kidnapped Robot Problem,” Proceedings of the Symposium on Discrete Algorithms, Miami, FL (2006) pp. 133142.
54.Koenig, S. and Simmons, R. G., “Unsupervised Learning of Probabilistic Models for Robot Navigation,” Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, MN, USA 1996) pp. 23012308.
55.Konolige, K., “Large-Scale Map-Making,” Proceedings of the National Conference on Artificial Intelligence, San Jose, CA (2004) pp. 457463.
56.Kortenkamp, D. and Weymouth, T., “Topological Mapping for Mobile Robots Using a Combination of Sonar and Vision Sensing,” Proceedings of the American Association for Artificial Intelligence (AAAI) Conference, Seattle, WA, USA (1994).
57.Kuipers, B., “The spatial semantic hierarchy,” Artif. Intell. 119, 191233 (2000).
58.Kuipers, B. and Beeson, P., “Bootstrap Learning for Place Recognition,” Proceedings of the 18th National Conference on Artificial Intelligence, Edmonton, Alberta, Canada (2002) pp. 174180.
59.Kuipers, B. and Byun, Y.-T., “A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations,” Robot. Auton. Syst. 8 (1), 4763 (1991).
60.Kuipers, B. and Levitt, T., “Navigation and mapping in large-scale space,” AI Mag., 9 (2), 2543 (1988).
61.Kuipers, B., Modayil, J., Beeson, P., MacMahon, M. and Savelli, F., “Local metrical and global topological maps in the hybrid spatial semantic hierarchy,” Proc. IEEE Int.Conf. Robot. Autom. 5, 48454851 (2004).
62.Lamon, P., Nourbakhsh, I., Jensen, B. and Siegwart, R., “Deriving and Matching Image Fingerprint Sequences for Mobile Robot Localization,” Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, Korea (2001).
63.Leonard, J. J. and Durrant-Whyte, H. F., “Mobile robot localization by tracking geometric beacons,” IEEE Trans. Robot. Autom. 7 (3), 376382 (1991a).
64.Leonard, J. J. and Durrant-Whyte, H. F., “Simultaneous Map Building and Localization for an Autonomous Mobile Robot,” Proceedings of the IEEE/RSJ International Workshop on Intelligent Robots and Systems, Osaka, Japan (1991b) pp. 14421447.
65.Li, Y. and Olson, E. B., “IPJC: The Incremental Posterior Joint Compatibility Test for Fast Feature Cloud Matching,” Proceedings of the IEEE/RSJ International Conference Intelligent Robots and Systems, Vilamoura, Algarve, Portugal (2012) pp. 34673474.
66.Lisien, B., Morales, D., Silver, D., Kantor, G., Rekleitis, I. and Choset, H., “Hierarchical Simultaneous Localization and Mapping,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV (2003) pp. 448453.
67.Liu, M., Scaramuzza, D., Pradalier, C., Siegwart, R. and Chen, Q., “Scene Recognition with Omnidirectional Vision for Topological Map Using Lightweight Adaptive Descriptors,” Proceedings of the IEEE/RSJ International Conference Intelligent Robots and Systems, St. Louis, MO, USA (2009) pp. 116121.
68.Liu, M. and Siegwart, R., “DP-FACT: Towards Topological Mapping and Scene Recognition with Color for Omnidirectional Camera,” IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA (2012).
69.Lowe, D. G., “Object recognition from local scale-invariant features,” Proc. IEEE Int. Conf. Computer Vision 2, 11501157.
70.Lowe, D. G., “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60 (2), 91110 (2004).
71.Lu, F. and Milios, E., “Globally consistent range scan alignment for environment mapping,” Auton. Robots 4, 333349 (1997).
72.Lui, W. L. D. and Jarvis, R., “A Pure Vision-Based Approach to Topological SLAM,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan (2010).
73.Maddern, W., Milford, M. and Wyeth, G., “Continuous Appearance-Based Trajectory SLAM,” Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China (2011) pp. 35953600.
74.Maddern, W., Milford, M. and Wyeth, G., “Capping Computation Time and Storage Requirements for Appearance-Based Localization with CAT-SLAM,” Proceedings of the IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA (2012a) pp. 822827.
75.Maddern, W., Milford, M. and Wyeth, G., “Towards Persistent Indoor Appearance-Based Localization, Mapping and Navigation Using CAT-SLAM,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Algarve, Portugal (2012b) pp. 42244230.
76.Maini, R. and Aggarwal, H., “Study and comparison of various image edge detection techniques,” Int. J. Image Process. 3 (1), 111 (2009).
77.Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T. and van Gool, L., “A comparison of affine region detectors,” Int. J. Comput. Vis. 65 (1), 4372 (2006).
78.Modayil, J., Beeson, P. and Kuipers, B., “Using the Topological Skeleton for Scalable Global Metrical Map-Building,” Proceedings of the IEEE/RSJ International Conference on lntelligent Robots and Systems, Sendai, Japan (2004) pp. 15301536.
79.Montemerlo, M., Thrun, S., Koller, D. and Wegbreit, B., “FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem,” Proceedings of the AAAI National Conference on Artificial Intelligence, Edmonton, Canada (2002).
80.Needleman, S. B. and Wunsch, C. D., “A general method applicable to the search for similarities in the amino acid sequence of two proteins,” J. Mol. Biol. 48, 443453 (1970).
81.Neira, J. and Tardós, J. D., “Data association in stochastic mapping using the joint compatibility test,” IEEE Trans. Robot. Autom. 17 (6), 890897 (2001).
82.Nguyen, V., Martinelli, A., Tomatis, N. and Siegwart, R., “A Comparison of Line Extraction Algorithms Using 2D Laser Rangefinder for Indoor Mobile Robotics,” Proceedings of the IEEE/RSJ International Conference Intelligent Robots and Systems, Edmonton, Canada (2005) pp. 19291934.
83.Nieto, J. I., Guivant, J. E. and Nebot, E. M., “The Hybrid Metric Maps (HYMMs): A novel map representation for DenseSLAM,” Proc. IEEE Int. Conf. Robot. Autom. 1, 391396 (2004).
84.Nüchter, A. and Hertzberg, J., “Towards semantic maps for mobile robots,” Robot. Auton. Syst. 56, 915926 (2008).
85.Owen, C. and Nehmzow, U., “Landmark-Based Navigation for a Mobile Robot,” In: Proceedings of the Simulation of Adaptive Behaviour (MIT Press, 1998) pp. 240245.
86.Paul, R. and Newman, P., “FAB-MAP 3D: Topological Mapping with Spatial and Visual Appearance,” Proceedings of the IEEE International Conference on Robotics and Automation, Anchorage, AK, USA (2010) pp. 26492656.
87.Pfister, S. T., Roumeliotis, S. I. and Burdick, J. W., “Weighted line fitting algorithms for mobile robot map building and efficient data representation,” Proc. IEEE Int. Conf. Robot. Autom. 1, 13041311 (2003).
88.Pirjanian, P., Karlsson, N., Goncalves, L. and di Bernardo, E., “Low-cost visual localization and mapping for consumer robotics,” Ind. Robot Int. J. 30 (2), 139144 (2003).
89.Ramos, F. T., Upcroft, B., Kumar, S. and Durrant-Whyte, H. F., “A Bayesian Approach for Place Recognition,” Proceedings of the IJCAI Workshop on Reasoning with Uncertainty in Robotics, Edinburgh, Scotland (2005).
90.Ranganathan, A., Probabilistic Topological Maps Ph.D. Thesis (Georgia Institute of Technology, 2008).
91.Ranganathan, A. and Dellaert, F., “Inference in the Space of Topological Maps: An MCMC-Based Approach,” Proceedings of the IEEE/RSJ International Conference Intelligent Robots and Systems, Sendai, Japan (2004) pp. 15181523.
92.Ranganathan, A. and Dellaert, F., “A Rao-Blackwellized Particle Filter for Topological Mapping,” Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, FL, USA (2006) pp. 810817.
93.Ranganathan, A. and Dellaert, F., Automatic Landmark Detection for Topological Mapping Using Bayesian Surprise Technical Report (Georgia Institute of Technology, 2008).
94.Ranganathan, A. and Dellaert, F., “Online probabilistic topological mapping,” Int. J. Robot. Res. 30 (6), 755771 (2011).
95.Ranganathan, A., Menegatti, E. and Dellaert, F., “Bayesian inference in the space of topological maps,” IEEE Trans. Robot. 22 (1), 92107 (2006).
96.Remolina, E. and Kuipers, B., “Towards a general theory of topological maps,” Artif. Intell. 152, 47104 (2004).
97.Romero, A. and Cazorla, M., “Topological SLAM Using Omnidirectional Images: Merging Feature Detectors and Graph-matching,” Proceedings of the Advanced Concepts for Intelligent Vision Systems, Sydney, Australia (2010) pp. 464475.
98.Romero, A. and Cazorla, M., “Topological visual mapping in robotics,” Cogn. Process. 13(1 Supplement), 305308 (2012).
99.Sabatta, D., Scaramuzza, D. and Siegwart, R., “Improved Appearance-Based Matching in Similar and Dynamic Environments Using a Vocabulary Tree,” Proceedings of the IEEE International Conference on Robotics and Automation, Anchorage, AK (2010).
100.Sabatta, D. G., “Vision-Based Topological Map Building and Localisation Using Persistent Features,” Robotics and Mechatronics Symposium, Bloemfontein, South Africa (2008) pp. 16.
101.Savelli, F. and Kuipers, B., “Loop-Closing and Planarity in Topological Map-Building,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (2004) pp. 15111517.
102.Se, S., Lowe, D. G. and Little, J. J., “Vision-based global localization and mapping for mobile robots,” IEEE Trans. Robot. 21 (3), 364375 (2005).
103.Shatkay, H. and Kaelbling, L. P., “Learning Topological Maps with Weak Local Odometric Information,” Proceedings of the International Conference on Artificial Intelligence, Nagoya, Japan (1997) pp. 920929.
104.Sivic, J. and Zisserman, A., “Video Google: A text retrieval approach to object matching in videos,” Proc. IEEE Int. Conf. Comput. Vis. 2, 14701477 (2003).
105.Stachniss, C., Martínez-Mozos, O., Rottmann, A. and Burgard, W., “Semantic Labeling of Places,” Proceedings of the International Symposium of Robotics Research, San Francisco, CA, USA (2005).
106.Stankiewicz, B. J. and Kalia, A. A., “Acquisition of structural versus object landmark knowledge,” J. Exp. Psychol. Hum. Perception Perform. 33 (2), 378390 (2007).
107.Tao, T., Tully, S., Kantor, G. and Choset, H., “Incremental Construction of the Saturated-GVG for Multi-Hypothesis Topological SLAM,” Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China (2011) pp. 30723077.
108.Tapus, A., Topological SLAM – Simultaneous Localization and Mapping with Fingerprints of Places Ph.D. Thesis (École Polytechnique Fédérale de Lausanne, Switzerland, 2005).
109.Tapus, A. and Siegwart, R., “Incremental Robot Mapping with Fingerprints of Places,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada (2005) pp. 24292434.
110.Thrun, S., “Robotic Mapping: A Survey,” In: Exploring Artificial Intelligence in the New Millenium (Lakemeyer, G. and Nebel, B., eds.) (Morgan Kaufmann, San Francisco, CA, 2002) pp. 135.
111.Tomatis, N., Nourbakhsh, I. and Siegwart, R., “Hybrid Simultaneous Localization and Map Building: Closing the Loop with Multi-Hypotheses Tracking,” Proceedings of the IEEE International Conference Robotics and Automation, Washington, DC (2002) pp. 27492754.
112.Tully, S., Kantor, G., Choset, H. and Werner, F., “A Multi-Hypothesis Topological SLAM Approach for Loop Closing on Edge-Ordered Graphs,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA (2009) pp. 49434948.
113.Ulrich, I. and Nourbakhsh, I., “Appearance-Based Place Recognition for Topological Localization,” Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA, USA (2000) pp. 10231029.
114.Vasudevan, S., Gáchter, S., Nguyen, V. and Siegwart, R., “Cognitive maps for mobile robots: an object based approach,” Robot. Auton. Syst. 55, 359371 (2007).
115.Werner, F., Gretton, C., Maire, F. and Sitte, J., “Induction of Topological Environment Maps from Sequences of Visited Places,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (2008a) pp. 28902895.
116.Werner, F., Maire, F. and Sitte, J., “Topological SLAM Using Fast Vision Techniques,” Proceedings of the Advances in Robotics: FIRA RoboWorld Congress, Incheon, Korea (2009a) pp. 187198.
117.Werner, F., Maire, F., Sitte, J., Choset, H., Tully, S. and Kantor, G., “Topological SLAM Using Neighbourhood Information of Places,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA (2009b).
118.Werner, F., Sitte, J. and Maire, F., “Visual Topological Mapping and Localisation Using Colour Histograms,” Proceedings of the International Conference on Control, Automation, Robotics and Vision, Hanoi, Vietnam (2008b) pp. 341346.
119.Werner, F., Sitte, J. and Maire, F., “Topological map induction using neighbourhood information of places,” Auton. Robots 32 (4), 405418 (2012).
120.Yamauchi, B., Schultz, A. and Adams, W., “Mobile Robot Exploration and Map-Building with Continuous Localization,” Proceedings of the International Conference on Robotics and Automation, Leuven, Belgium (1998).
121.Zivkovic, Z., Bakker, B. and Kröse, B., “Hierarchical Map Building Using Visual Landmarks and Geometric Constraints,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada (2005) pp. 24802485.

Keywords

Topological simultaneous localization and mapping: a survey

  • Jaime Boal (a1), Álvaro Sánchez-Miralles (a1) and Álvaro Arranz (a1)

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed