Artificial Intelligence Review

, Volume 43, Issue 1, pp 55–81 | Cite as

Visual simultaneous localization and mapping: a survey

  • Jorge Fuentes-Pacheco
  • José Ruiz-Ascencio
  • Juan Manuel Rendón-Mancha
Article

Abstract

Visual SLAM (simultaneous localization and mapping) refers to the problem of using images, as the only source of external information, in order to establish the position of a robot, a vehicle, or a moving camera in an environment, and at the same time, construct a representation of the explored zone. SLAM is an essential task for the autonomy of a robot. Nowadays, the problem of SLAM is considered solved when range sensors such as lasers or sonar are used to built 2D maps of small static environments. However SLAM for dynamic, complex and large scale environments, using vision as the sole external sensor, is an active area of research. The computer vision techniques employed in visual SLAM, such as detection, description and matching of salient features, image recognition and retrieval, among others, are still susceptible of improvement. The objective of this article is to provide new researchers in the field of visual SLAM a brief and comprehensible review of the state-of-the-art.

Keywords

Visual SLAM Salient feature selection Image matching Data association Topological and metric maps 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aguilar W, Frauel Y, Escolano F et al (2009) A robust graph matching for non-rigid registration. Image Vis Comput 27(7): 897–910CrossRefGoogle Scholar
  2. Andrade J, Sanfeliu A (2002) Concurrent map building and localization with landmark validation. In: Proceedings of the 16th IAPR international conference on pattern recognition, vol 2, pp 693–696Google Scholar
  3. Angeli A, Doncieux S, Filliat D (2008) Real time visual loop closure detection. In: Proceedings of the IEEE international conference on robotics and automationGoogle Scholar
  4. Angeli A, Doncieux S, Meyer J (2009) Visual topological SLAM and global localization. In: Proceedings of the IEEE international conference on robotics and automation, pp 4300–4305Google Scholar
  5. Artieda J, Sebastian J, Campoy P et al (2009) Visual 3-D SLAM from UAVs. J Intell Robot Syst 55(4): 299–321CrossRefMATHGoogle Scholar
  6. Asmar D (2006) Vision-inertial SLAM using natural features in outdoor environments. Dissertation, University of Waterloo, CanadaGoogle Scholar
  7. Auat C, Lopez N, Soria C, et al (2010) SLAM algorithm applied to robotics assistance for navigation in unknown environments. J Neuroeng Rehabil. doi:10.1186/1743-0003-7-10
  8. Bailey T, Durrant H (2006) Simultaneous localization and mapping (SLAM): Part II. IEEE Robot Autom Mag 13(3): 108–117CrossRefGoogle Scholar
  9. Bay H, Tuytelaars T, Van L (2006) SURF: speeded up robust features. In: Proceedings of the European conference on computer visionGoogle Scholar
  10. Bazeille S, Filliat D (2010) Combining odometry and visual loop-closure detection for consistent topo-metrical mapping. RAIRO Int J Oper Res 44(4): 365–377CrossRefGoogle Scholar
  11. Beis J, Lowe D (1997) Shape indexing using approximate nearest neighbour search in high-dimensional spaces. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1000–1006Google Scholar
  12. Bogdan R, Sundaresan A, Morisset B et al (2009) Leaving flatland: efficient real-time three-dimensional perception and motion planning. J Field Robot. Special Issue on Three-Dimensional Mapping 26(10): 841–862Google Scholar
  13. Bosse M, Newman P, Leonard J, et al (2003) An atlas framework for scalable mapping. In: Proceedings of the IEEE international conference on robotics and automation, pp 1899–1906Google Scholar
  14. Botterill T, Mills S, Green R (2010) Bag-of-words-driven single camera simultaneous localisation and mapping. J Field Robot 28(2): 204–226CrossRefGoogle Scholar
  15. Bouguet (2010) Camera calibration toolbox for matlab. http://www.vision.caltech.edu/bouguetj/calib_doc/. Accesed 06 March 2012.
  16. Brown MZ, Burschka D, Hager G (2003) Advances in computational stereo. IEEE Trans Pattern Anal Mach Intell 25(8): 993–1008CrossRefGoogle Scholar
  17. Cadena C, Gálvez-López D, Ramos F, et al (2010) Robust place recognition with stereo cameras. In: Proceedings of the IEEE international conference on intelligent robots and systems, pp 5182–5189Google Scholar
  18. Calonder M, Lepetit V, et al (2010) BRIEF: binary robust independent elementary features. In: Proceedings of the European conference on computer visionGoogle Scholar
  19. Cannons K (2008) A review of visual tracking. Technical report CSE-2008-07, York University, Department of Computer Science and EngineeringGoogle Scholar
  20. Carrera G, Angeli A, Andrew D (2011) SLAM-based automatic extrinsic calibration of a multi-camera rig. In: Proceedings of the IEEE international conference on robotics and automationGoogle Scholar
  21. Castellanos J, Tardós JD, Neira J (2001) Multisensor fusion for simultaneous localization and map building. IEEE Trans Robot Autom 17(6): 908–914CrossRefGoogle Scholar
  22. Ceriani S, Fontana G, Giusti A et al (2009) Rawseeds ground truth collection systems for indoor self-localization and mapping. J Auton Robots 27(4): 353–371CrossRefGoogle Scholar
  23. Chatila R, Laumond J (1985) Position referencing and consistent world modeling for mobile robots. In: Proceedings of the IEEE international conference on robotics and automation,vol 2, pp 138–145Google Scholar
  24. Chekhlov D, Mayol W, Calway A (2007) Ninja on a plane: automatic discovery of physical planes for augmented reality using visual SLAM. In: Proceedings of the 6th IEEE and ACM international symposium on mixed and augmented reality, pp 1–4Google Scholar
  25. Chekhlov D, Mayol W, Calway A (2008) Appearance based indexing for relocalisation in real-time visual SLAM. In: Proceedings of the British machine vision conference, pp 363–372Google Scholar
  26. Chli M, Davison A (2008) Active matching. In: Proceedings of the European conference on computer vision: part I. doi:10.1007/978-3-540-88682-2_7
  27. Chli M, Davison A (2009) Active matching for visual tracking. Robot Autonom Syst 57(12): 1173–1187CrossRefGoogle Scholar
  28. Ciganek B, Siebert J (2009) An introduction to 3D computer vision techniques and algorithms. Wiley, New York, pp 194–195Google Scholar
  29. Clemente L, Davison A, Reid I, et al (2007) Mapping large loops with a single hand-held camera. In: Proceedings of robotics: science and systems conferenceGoogle Scholar
  30. Collett M (2010) How desert ants use a visual landmark for guidance along a habitual route. In: Psychol Cogni Sci 107(25): 11638–11643Google Scholar
  31. Cummins (2008) New college and city centre dataset. http://www.robots.ox.ac.uk/~mobile/IJRR_2008_Dataset. Accesed 06 March 2012
  32. Cummins M, Newman P (2008) FAB-MAP: probabilistic localization and mapping in the space of appearance. Int J Robot Res 27(6): 647–665CrossRefGoogle Scholar
  33. Cyrill S (2009) Robotic mapping and exploration. Springer Tracts in Advanced Robotics, vol 55, ISBN: 978-3-642-01096-5Google Scholar
  34. Davison A (2003) Real-time simultaneous localisation and mapping with a single camera. In: Proceedings of the IEEE international conference on computer vision, vol2, pp 1403–1410Google Scholar
  35. Davison A, González Y, Kita N (2004) Real-time 3D SLAM with wide-angle vision. In: 5th IFAC/EURON symposium on intelligent autonomous vehiclesGoogle Scholar
  36. Davison A, Reid I, Molton N (2007) MonoSLAM: real-time single camera SLAM. IEEE Trans Pattern Anal Mach Intell 29(6): 1052–1067CrossRefGoogle Scholar
  37. Dufournaud Y, Schmid C, Horaud R (2004) Image matching with scale adjustment. Comput Vis Image Underst 93(2): 175–194CrossRefGoogle Scholar
  38. Durrant H, Bailey T (2006) Simultaneous localization and mapping (SLAM): part I the essential algorithms. IEEE Robot Autom Mag 13(2): 99–110CrossRefGoogle Scholar
  39. Eade E, Drummond T (2006a) Edge landmarks in monocular SLAM. In: Proceedings of the British machine vision conferenceGoogle Scholar
  40. Eade E, Drummond T (2006b) Scalable monocular SLAM. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 1, pp 469–476Google Scholar
  41. Eade E, Drummond T (2008) Unified loop closing and recovery for real time monocular SLAM. In Proceedings of the British Machine vision conferenceGoogle Scholar
  42. Engels C, Stewénius H, Nistér D (2006) Bundle adjustment rules. In: Photogrammetric computer visionGoogle Scholar
  43. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8): 861–874CrossRefMathSciNetGoogle Scholar
  44. Fraundorfer F, Engels C, Nister C (2007) Topological mapping, localization and navigation using image collections. In: Proceedings of the IEEE international conference on intelligent robots and systems, pp 3872–3877Google Scholar
  45. Frese U, Larsson P, Duckett T (2005) A multilevel relaxation algorithm for simultaneous localization and mapping. IEEE Trans Robot, pp 196–207, ISSN 1552-3098Google Scholar
  46. Frintrop S, Jensfelt P (2008) Attentional landmarks and active gaze control for visual SLAM. IEEE Trans Robot 24(5): 1054–1065CrossRefGoogle Scholar
  47. Gee A, Chekhlov D, Calway A, Mayol W (2008) Discovering higher level structure in visual SLAM. IEEE Trans Robot 24(5): 980–990CrossRefGoogle Scholar
  48. Gemeiner P, Davison A, Vincze M (2008) Improving localization robustness in monocular SLAM using a high-speed camera. In: Proceedings of robotics: science and systems IVGoogle Scholar
  49. Gil A, Martínez O, Ballesta M, Reinoso O (2009) A comparative evaluation of interest point detectors and local descriptors for visual SLAM. Mach Vis Appl 21(6): 905–920CrossRefGoogle Scholar
  50. Gil A, Reinoso O, Ballesta M, Juliá M (2010) Multi-robot visual SLAM using a rao-blackwellized particle filter. Robot Autonom Syst 58(1): 68–80CrossRefGoogle Scholar
  51. Glover A, Maddern W, Milford M, et al (2010) FAB-MAP + RatSLAM: appearance-based slam for multiple times of day. In: Proceedings of the IEEE international conference on robotics and automationGoogle Scholar
  52. Grasa O, Civera J, Montiel J (2011) EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proceedings of the IEEE international conference on robotics and automation, pp 4816–4821Google Scholar
  53. Grauman K (2010) Efficiently searching for similar images. Commun ACM 53(6): 84–94CrossRefGoogle Scholar
  54. Grauman K, Darrell T (2007) Pyramid match hashing: sub-linear time indexing over partial correspondences. In: Proceedings of the IEEE conference on computer vision and pattern recognitionGoogle Scholar
  55. Grisetti G, Kümmerle R, Stachniss C, Burgard W (2010) A tutorial on graph-based SLAM. IEEE Trans Intell Transp Syst Mag 2(4): 31–43CrossRefGoogle Scholar
  56. Gu S, Zheng Y, Tomasi C (2010) Critical nets and beta-stable features for image matching. In: Proceedings of the European conference on computer vision, pp 663–676Google Scholar
  57. Guivant J (2002) Efficient simultaneous localization and mapping in large environments. Dissertation, University of Sydney, AustraliaGoogle Scholar
  58. Gutmann J, Fukuchi M, Fujita M (2008) 3D Perception and environment map generation for humanoid robot. Int J Robot Res 27(10): 1117–1134CrossRefGoogle Scholar
  59. Handa A, Chli M, Strasdat H, Davison A (2010) Scalable active matching. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1546–1533Google Scholar
  60. Harris C, Stephens M (1988) A combined corner and edge detector. In: Proceedings of the fourth alvey vision conference, pp 147–151Google Scholar
  61. Hartley R, Sturm P (1997) Triangulation. Comput Vis Image Underst 68(2): 146–157CrossRefGoogle Scholar
  62. Hartley R, Zisserman A (2003) Multiple view geometry in computer vision, 2nd edn. Cambridge, ISBN: 0521540518Google Scholar
  63. Hinterstoisser S, Kutter O, Navab N, et al (2009) Real-time learning of accurate patch rectification. In: Proceedings of the IEEE conference on computer vision and pattern recognitionGoogle Scholar
  64. Ho K, Newman P (2007) Detecting loop closure with scene sequences. Int J Comput Vis 74(3): 261–286CrossRefGoogle Scholar
  65. Huang A, Bachrach A, Henry P, et al (2011) Visual odometry and mapping for autonomous flight using rgb-d camera. International symposium on robotics researchGoogle Scholar
  66. Johnson M, Pizarro O, Williams S, Mahon I (2010) Generation and visualization of large-scale three-dimensional reconstructions from underwater robotic surveys. J Field Robot 27(1): 21–51CrossRefGoogle Scholar
  67. Jones E, Soatto S (2011) Visual-inertial navigation, mapping and localization: a scalable real-time causal approach. Int J Robot Res 30(4): 407–430Google Scholar
  68. Kaess M, Dellaert F (2010) Probabilistic structure matching for visual SLAM with a multi-camera rig. Comput Vis Image Underst 114: 286–296CrossRefGoogle Scholar
  69. Kawewong A, Tangruamsub S, Hasegawa O (2010) Position-invariant robust features for long-term recognition of dynamic outdoor scenes. IEICE Trans Inform Syst 9: 2587–2601CrossRefGoogle Scholar
  70. Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 2, pp 506–513Google Scholar
  71. Klein G, Murray D (2007) Parallel tracking and mapping for small AR workspaces. In: Proceedings of the 6th IEEE and ACM international symposium on mixed and augmented realityGoogle Scholar
  72. Klein G, Murray D (2008) Improving the agility of keyframe-based SLAM. In: Proceedings of the European conference on computer vision, pp 802–815Google Scholar
  73. Koch O, Walter M, Huang A, Teller S (2010) Ground robot navigation using uncalibrated cameras. In: Proceedings of the IEEE international conference on robotics and automation, pp 2423–2430Google Scholar
  74. Konolige K, Agrawal M (2008) FrameSLAM: from bundle adjustment to real-time visual mapping. IEEE Trans Robot 24(5): 1066–1077CrossRefGoogle Scholar
  75. Konolige K, Bowman J, Chen J (2009) View-based maps, In: Proceedings of robotics: science and systemsGoogle Scholar
  76. Konolige K, Marder-Eppstein E, Marthi B (2011) Navigation in Hybrid metric- topological maps. In: Proceedings of the IEEE international conference on robotics and automationGoogle Scholar
  77. Kragic D, Vincze M (2009) Vision for robotics. Found Trends Robot 1(1):1–78, ISBN: 978-1-60198-260-5Google Scholar
  78. Kulis B, Jain P, Grauman K (2009) Fast similarity search for learned metrics. IEEE Trans Pattern Anal Mach Intell 31(12): 2143–2157CrossRefGoogle Scholar
  79. Lemaire T, Berger C, Jung I et al (2007) Vision-based SLAM: stereo and monocular approaches. Int J Comput Vis 74(3): 343–364CrossRefGoogle Scholar
  80. Lepetit V, Fua P (2005) Monocular model-based 3D tracking of rigid objects. Found Trends Comput Graph Comput Vis 1(1): 1–89CrossRefGoogle Scholar
  81. Lepetit V, Fua P (2006) Keypoint recognition using randomized trees. IEEE Trans Pattern Anal Mach Intell 28(9): 1465–1479CrossRefGoogle Scholar
  82. Li H, Kimi E, Huang X, He L (2010) Object matching with a locally affine-invariant constraint In: Proceedings of the International conference on pattern recognition, pp 1641–1648Google Scholar
  83. Lin K, Wang C (2010) Stereo-based simultaneous localization, mapping and moving object tracking. In: Proceedings of the IEEE international conference on intelligent robots and systems, pp 3975–3980Google Scholar
  84. Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2): 91–110CrossRefGoogle Scholar
  85. Magnusson M, Andreasson H, et al (2009) Automatic appearance-based loop detection from 3D laser data using the normal distribution transform. J Field Robot. Three Dimensional Mapping Part 2, 26(12): 892–914Google Scholar
  86. Manning C, Schütze H, Raghavan P (2008) Introduction to information retrieval, Cambridge University Press, Cambridge, ISBN: 0521865719Google Scholar
  87. Majumder S, Scheding S, Durrant H (2005) Sensor fusion and map building for underwater navigation. In: Proceedings of Australian conference on robotics and automationGoogle Scholar
  88. Martinez J, Calway (2010) A unifying planar and point mapping in monocular SLAM. In: Proceedings of the British machine vision conference, pp 1–11Google Scholar
  89. Matas J, Chum O, et al (2002) Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the British machine vision conference vol 22, no. 10, pp 761–767Google Scholar
  90. Mei C, Reid I (2008) Modeling and generating complex motion blur for real-time tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–8Google Scholar
  91. Mei C, Sibley G, Cummins M, et al (2009) A constant-time efficient stereo SLAM system. In: Proceedings of the British machine vision conferenceGoogle Scholar
  92. Mei C, Sibley G, Cummins M et al (2010) RSLAM: a system for large-scale mapping in constant-time using stereo. Int J Comput Vision 94(2): 1–17Google Scholar
  93. Mei C, Sommerlade E, Sibley C, et al (2011) Hidden view synthesis using real-time visual SLAM for simplifying video surveillance analysis. In: Proceedings of the IEEE International conference on robotics and automation, vol 8, pp 4240–4245Google Scholar
  94. Migliore D, Rigamonti R, Marzorati D, et al (2009) Use a single camera for simultaneous localization and mapping with mobile object tracking~in dynamic environments. In: ICRA workshop on safe navigation in open and dynamic environments: application to autonomous vehiclesGoogle Scholar
  95. Mikolajcczyk K, Schmid C (2002) An affine invariant interest point detector. In: Proceedings of the European conference on computer vision, pp 128–142Google Scholar
  96. Mikolajcczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10): 1615–1630CrossRefGoogle Scholar
  97. Mikolajczyk K, Tuytelaars T, Schmid S et al (2005) A comparision of affine region detectors. Int J Comput Vis 65: 43–72CrossRefGoogle Scholar
  98. Milford M (2008) Robot navigation from nature: simultaneous, localisation, mapping, and path planning based on hippocampal models, vol. 41. Springer Tracts in Advanced Robotics, ISBN: 3540775196Google Scholar
  99. Milford M, Wyeth G (2008) Mapping a suburb with a single camera using a biologically inspired SLAM system. IEEE Trans Robot 24(5): 1038–1053CrossRefGoogle Scholar
  100. Milford M, Wyeth G, Prasser D (2004) RatSLAM: a hippocampal model for simultaneous localization and mapping. In: Proceeding of the IEEE international conference on robotics and automation, vol 1, pp 403–408Google Scholar
  101. Molton N, Davison A, Reid I (2004) Locally planar patch features for real-time structure from motion. In: Proceedings of the British machine vision conferenceGoogle Scholar
  102. Montemerlo M (2003) FastSLAM: a factored solution to the simultaneous localization and mapping problem with unknown data association, Dissertation, Carnegie Mellon University, USAGoogle Scholar
  103. Montemerlo M, Thrun S, Koller D, et al (2002) FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: Proceedings of the AAAI national conference on artificial intelligence, pp 593–598Google Scholar
  104. Montiel J, Civera J, Davison A (2006) Unified inverse depth parametrization for monocular SLAM. In: Proceedings of robotics: science and systemsGoogle Scholar
  105. Morel J, Yu G (2009) ASIFT: a new framework for fully affine invariant image comparison. SIAM J Imaging Sci 2(2): 438–469CrossRefMathSciNetMATHGoogle Scholar
  106. Moreels P, Perona P (2005) Evaluation of features detectors and descriptors based on 3D objects. In: Proceedings of the IEEE international conference on computer vision, pp 800–807Google Scholar
  107. Mouragnon E, Dhome M, Dekeyser F, et al (2006) Monocular vision based SLAM for mobile robots. In: Proceedings of the international conference on pattern recognition, pp 1027–1031Google Scholar
  108. Mouragnon E, Lhuillier M, Dhome M, et al (2009) Generic and real time structure from motion using local bundle asjustment. Image Vis Comput, pp 1178–1193, ISSN: 0262-8856Google Scholar
  109. Neira J, Tardós JD (2001) Data association in stochastic mapping using the joint compatibility test. In: Proceedings of the IEEE international conference on robotics and automation 17(6): 890–897Google Scholar
  110. Newman P, Leonard J, Neira J, Tardós J (2002) Explore and return: experimental validation of real time concurrent mapping and localization. In: Proceedings of the IEEE international conference on robotics and automation, vol 2, pp 1802–1809Google Scholar
  111. Nistér D (2004) An efficient solution to the five-point relative pose problem. IEEE Trans Pattern Anal Mach Intell 26(6): 756–770CrossRefGoogle Scholar
  112. Nistér D, Stewenius H (2006) Scalable recognition with a vocabulary tree. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 2, pp 2161–2168Google Scholar
  113. Nistér D, Naroditsky O, Bergen J (2004) Visual odometry. In: Proceedings of the IEEE conference on computer vision and pattern recognition vol 1, pp 652–659Google Scholar
  114. Nüchter A, Lingemann K, Hertzberg J et al (2007) 6D SLAM—3D mapping outdoor environments. J Field Robot 24(8): 699–722CrossRefMATHGoogle Scholar
  115. Nützi G, Weiss S, Scaramuzza D, Siegwart R (2010) Fusion of IMU and vision for absolute scale estimation in monocular SLAM. J Intell Robot Syst. doi:10.1007/s10846-010-9490-z
  116. Olson C, Matthies L, Schoppers M, Maimone M (2003) Rover navigation using stereo ego-motion. Robot Autonom Syst 43(4): 215–229CrossRefGoogle Scholar
  117. Olson E, Leonard J, Teller S (2006) Fast iterative optimization of pose graphs with poor initial estimates. In: Proceedings of the IEEE international conference on robotics and automation, pp 2262-2269Google Scholar
  118. Olson C, Matthies L, Wright J et al (2007) Visual terrain mapping for mars exploration. Comput Vis Image Underst 105(1): 73–85CrossRefGoogle Scholar
  119. OpenCV (2009) OpenCV: Camera calibration and 3D reconstruction. http://opencv.willowgarage.com/documentation/camera_calibration_and_3d_reconstruction.html Accesed 06 March 2012
  120. Özuysal M, Calonder M, Lepetit V, Fua P (2010) Fast keypoint recognition using random ferns. IEEE Trans Pattern Anal Mach Intell 32(3): 448–461CrossRefGoogle Scholar
  121. Paz L, Piniés P, Tardós JD, Neira J (2008) Large-scale 6DOF SLAM with stereo-in-hand. IEEE Trans Robot 24(5): 946–957CrossRefGoogle Scholar
  122. Piniés P, Tardós JD, Neira J (2006) Localization of avalanche victims using robocentric SLAM. In: Proceedings of the IEEE international conference on intelligent robots and systems. pp 3074–3079Google Scholar
  123. Piniés P, Tardós JD (2008) Large scale SLAM building conditionally independent local maps: application to monocular vision. IEEE Trans Robot 24(5): 1094–1106CrossRefGoogle Scholar
  124. Pollefeys M, Van L, Vergauwen M et al (2004) Visual modeling with a hand-held camera. Int J Comput Vis 59(3): 207–232CrossRefGoogle Scholar
  125. Pretto A, Menegatti E, Pagello E (2007) Reliable features matching for humanoid robots. In: IEEE-RAS international conference on humanoid robots, pp 532–538Google Scholar
  126. Pupilli M, Calway A (2006) Real-time visual SLAM with resilience to erratic motion. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 1, pp 1244–1249Google Scholar
  127. Raguram R, Frahm J, Pollefeys M (2008) A comparative analysis of RANSAC techniques leading to adaptive real-time random sample consensus. In: Proceedings of the European conference on computer vision, pp 500–513Google Scholar
  128. Rawseeds (2012) Bovisa and bicocca datasets. http://www.rawseeds.org/rs/datasets. Accesed 06 March 2012
  129. Ribas D, Ridao P, Tardós JD et al (2008) Underwater SLAM in man-made structured environments. J Field Robot 25(11): 898–921CrossRefMATHGoogle Scholar
  130. Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In: Proceedings of the European conference on computer vision, pp 430–443Google Scholar
  131. Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: an efficient alternative to SIFT or SURF. In: Proceedings of the IEEE international conference on computer visionGoogle Scholar
  132. Scaramuzza (2011) OcamCalib toolbox: omnidirectional camera and calibration toolbox for matlab. https://sites.google.com/site/scarabotix/ocamcalib-toolbox. Accesed 06 March 2012
  133. Scaramuzza D, Siegwart R (2008) Appearance guided monocular omnidirectional visual odometry for outdoor ground vehicles. IEEE Trans Robot 24(5): 1015–1026CrossRefGoogle Scholar
  134. Se S, Lowe D, Little J (2002) Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. Int J Robot Res 21(8): 735–758CrossRefGoogle Scholar
  135. Se S, Lowe D, Little J (2005) Vision- based global localization and mapping for mobile robots. IEEE Trans Robot 21(3): 364–375CrossRefGoogle Scholar
  136. Sáez J, Escolano F (2006) 6DOF entropy minimization SLAM. In: Proceedings of the IEEE international conference on robotics and automation, pp 1548–1555Google Scholar
  137. Sanromá G, Alquézar R, Serratosa F (2010) Graph matching using SIFT descriptors—an application to pose recovery of a mobile robot. In: 13th joint IAPR international workshop on structural, syntactic and statistical pattern recognition, pp 254–263Google Scholar
  138. Silpa C, Hartley R (2008) Optimised KD-trees for fast image descriptor matching. In: Proceedings of the IEEE conference on computer vision and pattern recognitionGoogle Scholar
  139. Sinha S, Frahm J, Pollefeys M, Genc Y (2006) GPU-based video feature tracking and matching. In: Workshop on edge computing using new commodity architecturesGoogle Scholar
  140. Sivic J, Zisserman A (2003) Video google: a text retrieval approach to object matching in videos. In: Proceedings of the IEEE international conference on computer visionGoogle Scholar
  141. Simpson R, Cullip J, Revell J (2012) The cheddar gorge data set. http://www.openslam.org/misc/BAE_RSJCJR_2011.pdf. Accesed 06 March 2012
  142. Smith (2012) The new college vision and laser data set. http://www.robots.ox.ac.uk/NewCollegeData/. Accesed 06 March 2012
  143. Smith R, Self M, Cheeseman P (1990) Estimating uncertain spatial relationships in robotics. In: Autonomous robot vehicles. Springer, New York, pp 167–193, ISBN:0-387-97240-4Google Scholar
  144. Smith M, Baldwin I, Churchill W et al (2009) The new college vision and laser data set. Int J Robot Res 28(5): 595–599CrossRefGoogle Scholar
  145. Solà J (2007) Multi-camera VSLAM: from former information losses to self-calibration. In: Proceedings of the IEEE international conference on intelligent robots and systems, workshop on visual SLAMGoogle Scholar
  146. Steder B, Grisetti G, Stachniss C et al (2008) Visual SLAM for flying vehicles. IEEE Trans Robot 24(5): 1088–1093CrossRefGoogle Scholar
  147. Sturm (2012) RGB-D dataset and benchmark. http://cvpr.in.tum.de/data/datasets/rgbd-dataset. Accesed 06 March 2012
  148. Sturm J, Magnenat S, et al (2011) Towards a benchmark for RGB-D SLAM evaluation. In: Proceedings of the RGB-D workshop on advanced reasoning with depth cameras at robotics: science and systems conferenceGoogle Scholar
  149. Strasdat H, Montiel J, Davison A (2010a) Scale drift-aware large scale monocular SLAM. In Proceeding of robotics: science and systemsGoogle Scholar
  150. Strasdat H, Montiel J, Davison A (2010b) Real-time monocular SLAM: why filter?. In: Proceedings of the IEEE international conference on robotics and automationGoogle Scholar
  151. Svoboda (2011) Multi-camera self-calibration. http://cmp.felk.cvut.cz/~svoboda/SelfCal/index.html Accesed 06 March 2012
  152. Tardós JD, Neira J, Newman P et al (2002) Robust mapping and localization in indoor environments using sonar data. Int J Robot Res 21: 311–330CrossRefGoogle Scholar
  153. Taylor S, Drummond T (2009) Multiple target localization at over 100 FPS. In: Proceedings of the British machine vision conferenceGoogle Scholar
  154. Thrun S (2002) Robotic mapping: a survey. Exploring artificial intelligence in the new millennium, ISBN:1-55860-811-7Google Scholar
  155. Thrun S (2003) A system for volumetric robotic mapping of abandoned mines. In: Proceedings of the IEEE international conference on robotics and automation, vol 3, pp 4270–4275Google Scholar
  156. Thrun S, Leonard J (2008) Simultaneous localization and mapping. Springer Handbook of Robotics; Siciliano, Khatib Editors, ISBN: 978-3-540-23957-4, pp 871–886Google Scholar
  157. Thrun S, Koller D, Ghahramani Z, et al (2002) Simultaneous mapping and localization with sparse extended information filters: theory and initial results. Technical Report CMU-CS-02-112, Carnegie MellonGoogle Scholar
  158. Thrun S, Montemerlo M, Dahlkamp H et al (2005a) Stanley: the robot that won the~DARPA grand challenge. J Field Robot 23(9): 661–692CrossRefGoogle Scholar
  159. Thrun S, Burgard W, Fox D, (2005b) Probabilistic Robotics. The MIT Press, New York, ISBN: 0262201623Google Scholar
  160. Thrun S, Montemerlo M, Aron A (2006) Probabilistic terrain analysis for high speed desert driving. In: Proceedings of robotics: science and systemsGoogle Scholar
  161. Tirilly P, Claveau V, Gros P (2010) Distances and weighting schemes for bag of visual words image retrieval. In: Proceedings of the international conference on multimedia information retrieval, pp 323–333Google Scholar
  162. Triggs B, Mclauchlan P, Hartley R, Fitzgibbon A (1999) Bundle adjustment—a modern synthesis. In: Proceedings of the international workshop on vision algorithms: theory and practice, pp 298–375Google Scholar
  163. Tuytelaars T, Mikolajczyk K (2008) Local invariant feature detectors: a survey. Found Trends Comput Graph VisGoogle Scholar
  164. Tuytelaars T, Van-Gool L (2004) Matching widely separated views based on affine invariant regions. Int J Comput Vis 59(1): 61–85CrossRefGoogle Scholar
  165. Vidal T, Bryson M, Sukkarieh S, et al (2007) On the observability of bearing-only SLAM. In: Proceedings of the IEEE international conference on robotics and automation, pp 4114–4119Google Scholar
  166. Vidal T, Berger C, Sola J, Lacroix S (2011) Large scale multiple robot visual mapping with heterogeneous landmarks in semi-structured terrain. Robot Autonom Syst, pp 654–674Google Scholar
  167. Wang C, Thorpe Ch, Thrun S et al (2007) Simultaneous localization, mapping and moving object tracking. Int J Robot Res 26(9): 889–916CrossRefGoogle Scholar
  168. Wangsiripitak S, Murray D (2009) Avoiding moving outliers in visual SLAM by tracking moving objects. In: Proceedings of the IEEE international conference on robotics and automation, pp 375–380Google Scholar
  169. Williams B (2009) Simultaneous localisation and mapping using a single camera. PhD, thesis, Oxford University, EnglandGoogle Scholar
  170. Williams B, Klein G, Reid I (2007) Real-time SLAM relocalisation. In: Proceedings of the IEEE international conference on computer visionGoogle Scholar
  171. Williams B, Cummins M, Neira J, Newman P, Reid I, Tardós JD (2009) A comparision of loop closing techniques in monocular SLAM. Robot Autonom Syst 57(12): 1188–1197CrossRefGoogle Scholar
  172. Willson (1995) Tsai camera calibration software. http://www.cs.cmu.edu/~rgw/TsaiCode.html. Accesed 06 March 2012
  173. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4): 1–45CrossRefGoogle Scholar
  174. Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11): 1330–1334CrossRefGoogle Scholar
  175. Zhang W, Kosecka J (2006) Image based localization in urban environments. In: Proceedings of the third international symposium on 3d data processing, visualization, and transmissionGoogle Scholar
  176. Zhang Z, Deriche R, Faugeras O, Luong Q (1994) A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. J Artif Intell. Special volume on Computer Vision 78(1): 87–119Google Scholar
  177. Zhang H, Li B, Yang D (2010) Keyframe detection for appearance-based visual SLAM. In: Proceedings of the IEEE international conference on intelligent robots and systems, pp 2071–2076Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Jorge Fuentes-Pacheco
    • 1
  • José Ruiz-Ascencio
    • 1
  • Juan Manuel Rendón-Mancha
    • 2
  1. 1.Centro Nacional de Investigación y Desarrollo TecnológicoCuernavacaMéxico
  2. 2.Universidad Autónoma del Estado de MorelosCuernavacaMéxico

Personalised recommendations