The DOI matches its Crossref record; 28 of the 29 references it cites were found in a registry or an index.
A solution to the simultaneous localization and map building (SLAM) problem
Abstract
The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from estimation-theoretic foundations of this problem, the paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. The paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeter-wave radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are cross-compared with absolute locations of the map landmarks obtained by surveying. In conclusion, the paper discusses a number of key issues raised by the solution to the SLAM problem including suboptimal map-building algorithms and map management.
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“In light of this drawback Montemerlo et al. [195] introduced FastSLAM, an algorithm that recursively approximates the entire posterior distribution over robot pose and landmark locations, yet scales exponentially with the number of landmarks on the map.”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as Montemerlo et al. [195]
In the citing paper
“FastSLAM uses a hybrid technique that integrates Particle Filter and EKF approach, making it popular for its higher data accuracy [193, 195].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [195]
Citations
Cites
A robust layered control system for a mobile robot
Brooks, R. (1986) · IEEE Journal on Robotics and Automation · doi:10.1109/jra.1986.1087032 · Cited by 7,799
Stochastic models, estimation, and control
(1982) · Mathematics in Science and Engineering/Mathematics in science and engineering · doi:10.1016/s0076-5392(08)x6186-7 · Cited by 3,230
Bar‐Shalom, Y. (1988) · Medical Entomology and Zoology · Cited by 3,157
Bar‐Shalom, Y., Fortmann, T. E., & Cable, P. G. (1990) · The Journal of the Acoustical Society of America · doi:10.1121/1.398863 · Cited by 3,088
Iterative point matching for registration of free-form curves and surfaces
Zhang, Z. (1994) · International Journal of Computer Vision · doi:10.1007/bf01427149 · Cited by 2,438
On the Representation and Estimation of Spatial Uncertainty
Smith, R. C., & Cheeseman, P. C. (1986) · The International Journal of Robotics Research · doi:10.1177/027836498600500404 · Cited by 1,624
Estimating Uncertain Spatial Relationships in Robotics
Smith, R. K., Self, M., & Cheeseman, P. (1990) · Springer eBooks · doi:10.1007/978-1-4613-8997-2_14 · Cited by 1,289
Directed Sonar Sensing for Mobile Robot Navigation
Leonard, J. J., & Durrant-Whyte, H. F. (1992) · Springer eBooks · doi:10.1007/978-1-4615-3652-9 · Cited by 798
Self-citationA Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Thrun, S., Burgard, W., & Fox, D. (1998) · Machine Learning · doi:10.1023/a:1007436523611 · Cited by 795
Estimating uncertain spatial relationships in robotics
Smith, R., Self, M., & Cheeseman, P. (2005) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1987.1087846 · Cited by 761
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Real-time appearance-based Monte Carlo localization
Linåker, F., & Ishikawa, M. (2005) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2005.11.003 · Cited by 22
A region-based SLAM algorithm capturing metric, topological, and semantic properties
Oberlander, J., Uhl, K., Zöllner, J. M., & Dillmann, R. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543482 · Cited by 22
Adaptive Source Localization by a Mobile Robot Using Signal Power Gradient in Sensor Networks
Sun, Y., Xiao, J., Li, X., & Cabrera-Mora, F. (2008) · Globecom · doi:10.1109/glocom.2008.ecp.12 · Cited by 22
Hardegger, M., Roggen, D., Calatroni, A., & Tröster, G. (2016) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2824286 · Cited by 22
Jung, M., Myung, H., & Hong, S.-G. (2005) · International Symposium on Micro-NanoMechatronics and Human Science · doi:10.1109/mhs.2004.1421267 · Cited by 22
ROBHAZ-rescue : rough-terrain negotiable teleoperated mobile robot for rescue mission
Kang, S., Lee, W., Kim, M., & Shin, K. (2005) · IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) · doi:10.1109/ssrr.2005.1501248 · Cited by 22
Planetary Monocular Simultaneous Localization and Mapping
Bajpai, A., Burroughes, G., Shaukat, A., & Gao, Y. (2015) · Journal of Field Robotics · doi:10.1002/rob.21608 · Cited by 21
Ardentov, A. A., Караваев, Ю. Л., & Yefremov, K. S. (2019) · Regular and Chaotic Dynamics · doi:10.1134/s1560354719030055 · Cited by 21
Towards persistent visual navigation using SMART
Pepperell, E., Corke, P., & Milford, M. J. (2013) · QUT ePrints (Queensland University of Technology) · Cited by 22
6-DoF Low Dimensionality SLAM (L-SLAM)
Zikos, N., & Petridis, V. (2014) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-014-0029-6 · Cited by 21
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Ding, W., Wang, J., & Almagbile, A. (2010) · International Conference on Electrical and Control Engineering · doi:10.1109/icece.2010.1117 · Cited by 21
A tag-free solution to unobtrusive indoor tracking using wall-mounted ultrasonic transducers
Wan, E. A., & Paul, A. S. (2010) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2010.5648178 · Cited by 21
Design Methods for Augmented Reality In-Vehicle Infotainment Systems
Rao, Q., Grünler, C., Hammori, M., & Chakraborty, S. (2014) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/2593069.2602973 · Cited by 21
Khan, U. A., Kar, S., & Moura, J. M. F. (2015) · IEEE Access · doi:10.1109/access.2015.2464812 · Cited by 21
Integrity of laser-based feature extraction and data association
Joerger, M., Jamoom, M. B., Spenko, M. J., & PERVAN, B. S. (2016) · IEEE/ION Position Location and Navigation Symposium · doi:10.1109/plans.2016.7479746 · Cited by 23
Random Set Approach to Distributed Multivehicle SLAM
Battistelli, G., Chisci, L., & Laurenzi, A. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.410 · Cited by 21
RadarSLAM: Biomimetic SLAM using ultra-wideband pulse-echo radar
Schouten, G., & Steckel, J. (2017) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2017.8115932 · Cited by 21
Pedestrian Navigation System for Visually Impaired People Using HoloLens and RFID
Yamashita, A., Sato, K., Sato, S., & Matsubayashi, K. (2017) · Conference on Technologies and Applications of Artificial Intelligence (TAAI) · doi:10.1109/taai.2017.9 · Cited by 21
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Badriasl, L., Arulampalam, S., van der Hoek, J., & Finn, A. (2019) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2019.2917863 · Cited by 21
Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation
Watkins-Valls, D., Xu, J., Waytowich, N. R., & Allen, P. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341511 · Cited by 21
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Sugiura, K., & Matsutani, H. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2020edp7174 · Cited by 21
Towards a Future Robotic Home Environment: A Survey
Güttler, J., Georgoulas, C., Linner, T., & Bock, T. A. (2014) · Gerontology · doi:10.1159/000363698 · Cited by 21
An efficient data association approach to simultaneous localization and map building
Zhang, S., Xie, L., & Adams, M. D. (2004) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2004.1307256 · Cited by 21
Song, D., Kim, C.-Y., & Yi, J. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152557 · Cited by 21
Dual kalman filters for autonomous terrain aided navigation in unknown environments
Paul, A. S., & Wan, E. A. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1556366 · Cited by 21
Bearing-only SLAM in Indoor Environments Using a Modified Particle Filter
Kwok, N. M., & Dissanayake, G. (2003) · UTS ePRESS (University of Technology Sydney) · Cited by 21
Autonomous and teleoperation control of a mobile robot
Carelli, R., Forte, G. O., Canali, L. R., Mut, V. A., Araguás, G. R., & Destéfanis, E. A. (2008) · Mechatronics · doi:10.1016/j.mechatronics.2008.01.002 · Cited by 21
Simulation research for active Simultaneous Localization and Mapping based on Extended Kalman Filter
Xi, B., Guo, R., Sun, F., & Huang, Y. (2008) · IEEE International Conference on Automation and Logistics (ICAL) · doi:10.1109/ical.2008.4636578 · Cited by 21
Underwater Localization and Environment Mapping Using Wireless Robots
Wang, S., Chen, L., Hu, H., Xue, Z., & Pan, W. (2013) · Wireless Personal Communications · doi:10.1007/s11277-013-1106-z · Cited by 20
Robotic Mapping and Localization Considering Unknown Noise Statistics
Ahmad, H., & Namerikawa, T. (2011) · Journal of System Design and Dynamics · doi:10.1299/jsdd.5.70 · Cited by 20
Visual Homing Navigation With Haar-Like Features in the Snapshot
Lee, C.-M., & Kim, D. (2018) · IEEE Access · doi:10.1109/access.2018.2842679 · Cited by 17
Quantifying Safety of Laser-Based Navigation
Joerger, M., & PERVAN, B. S. (2018) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2018.2850381 · Cited by 22
Mobile Robot Path Planning Using Continuous Laser Scanning
Ivanov, M., Lindner, L., Sergiyenko, O., Rodriguez-Quinonez, J. C., Flores‐Fuentes, W., & Rivas-López, M. (2018) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-5225-5751-7.ch012 · Cited by 20
Indoor Positioning Using Magnetic Fingerprint Map Captured by Magnetic Sensor Array
Chen, C.-H., Chen, P.-W., Chen, P.-W., & Liu, T.-H. (2021) · Sensors · doi:10.3390/s21175707 · Cited by 20
Ren, Z., & Wang, L. (2022) · Sensors · doi:10.3390/s22041463 · Cited by 20
Bucci, A., Ridolfi, A., & Allotta, B. (2024) · Journal of Field Robotics · doi:10.1002/rob.22375 · Cited by 20
Brain-inspired sensor fusion for navigating robots
Milford, M. J., & Jacobson, A. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630980 · Cited by 20
Robotic SHM and Model-Based Positioning System for Monitoring and Construction Automation
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Multi-robot cooperative map building in unknown environment considering estimation uncertainty
Tong, T., Huang, Y., Jing, Y., & Fengchi, S. (2008) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2008.4597854 · Cited by 20
Gentner, C., & Ulmschneider, M. (2017) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2017.8115959 · Cited by 20
Bounds on RF cooperative localization for video capsule endoscopy
Ye, Y. (2013) · Digital WPI · Cited by 20
Visual SLAM with RGB-D Cameras
Jin, Q., Liu, Y., Man, Y., & Li, F. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8865270 · Cited by 20
Grid Map Construction and Terrain Prediction for Quadruped Robot Based on C-Terrain Path
Li, Z., Li, Y., Rong, X., & Hui, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2977396 · Cited by 20
ICS: Incremental Constrained Smoothing for State Estimation
Sodhi, P., Choudhury, S., Mangelson, J. G., & Kaess, M. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196649 · Cited by 20
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Leitinger, E., & Meyer, F. (2020) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf51394.2020.9443537 · Cited by 20
Hybrid Free-Space Optics/Radio Frequency (FSO/RF) Networks for Mobile Robot Teams
Derenick, J. C., Thorne, C., & Spletzer, J. R. (2005) · Springer eBooks · doi:10.1007/1-4020-3389-3_22 · Cited by 20
Ippoliti, G., Jetto, L., Manna, A. L., & Longhi, S. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2005.1570319 · Cited by 20
Online Robot Navigation Using Continuously Updated Artificial Temperature Gradients
Golan, Y., Edelman, S., Shapiro, A., & Rimon, E. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2665682 · Cited by 20
A MOBILE ROBOT MAPPING SYSTEM WITH AN INFORMATION-BASED EXPLORATION STRATEGY
Francesco Amigoni, A., Caglioti, V., & Galtarossa, U. (2004) · International Conference on Informatics in Control, Automation and Robotics (ICINCO) · doi:10.5220/0001141900710078 · Cited by 20
Global urban localization of outdoor mobile robots using satellite images
Doğruer, C. U., Koku, A. B., & Dölen, M. (2008) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2008.4650983 · Cited by 20
Alnajjar, F. S. K., Zin, I. B. M., & Murase, K. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634103 · Cited by 20
Active gaze control for attentional visual SLAM
Frintrop, S., & Jensfelt, P. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543777 · Cited by 20
Autonomous Offroad Navigation Under Poor GPS Conditions
Luettel, T., Himmelsbach, M., Hundelshausen, F. V., Manz, M. G., Mueller, A., & Wuensche, H. (2009) · Cited by 20
Online Localization with Imprecise Floor Space Maps using Stochastic Gradient Descent
Li, Z., Ang, M. H., & Rus, D. L. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340793 · Cited by 19
Omni-Roach: A Legged Robot Capable of Traversing Multiple Types of Large Obstacles and Self-Righting
Mi, J., Wang, Y., & Li, C. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811372 · Cited by 20
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Soni, A., Dasannacharya, C., Gautam, A., Shekhawat, V. S., & Mohan, S. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981914 · Cited by 19
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Georgiou, C., Anderson, S., & Dodd, T. J. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364916687027 · Cited by 19
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Porta, J. M., & Kröse, B. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2004.1389946 · Cited by 19
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Lee, D., Kim, D., Lee, S., Myung, H., & Choi, H. (2013) · International Conference on Ubiquitous Robots and Ambient Intelligence · doi:10.1109/urai.2013.6677329 · Cited by 19
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Localization and navigation of a mobile robot in an office-like environment
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Cooperative Multi-Robot Systems A study of Vision-based 3-D Mapping using Information Theory
Rocha, R. P., Dias, J. M. M., & Carvalho, A. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2005.1570149 · Cited by 19
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Eskandari, M., & Savkin, A. V. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3298607 · Cited by 19
Hsieh, C.-C., Chen, H.-M., & Wang, S. (2023) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-023-1939-2 · Cited by 18
Multi-sensor information fusion in Internet of Vehicles based on deep learning: A review
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Hu, W., Downs, T., Wyeth, G. F., Milford, M. J., & Prasser, D. (2004) · QUT ePrints (Queensland University of Technology) · Cited by 18
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Chen, Z., Jacobson, A., Erdem, U. M., Hasselmo, M. E., & Milford, M. J. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907109 · Cited by 18
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Perera, L. D. L., Wijesoma, W. S., & Adams, M. D. (2009) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2009.2026165 · Cited by 18
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He, B., Zhang, S., Yan, T., Zhang, T., Liang, Y., & Zhang, H. (2011) · Sensors · doi:10.3390/s111110197 · Cited by 18
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Bronzino, G. P. C., Grasso, N., Matrone, F., Osello, A., & Piras, M. (2019) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w15-215-2019 · Cited by 18
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Li, X., Aouf, N., & Nemra, A. (2012) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med.2012.6265662 · Cited by 18
Madjid Tavana, D., & Bourgeois, B. S. (2010) · International Journal of Operational Research · doi:10.1504/ijor.2010.030804 · Cited by 18
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Augenstein, S., & Rock, S. M. (2009) · AIAA Guidance, Navigation, and Control Conference and Exhibit · doi:10.2514/6.2009-5782 · Cited by 18
Nygårds, J., Skoglar, P., Ulvklo, M., & Högström, T. (2004) · Journal of Robotic Systems · doi:10.1002/rob.10128 · Cited by 18
An AEKF-SLAM Algorithm with Recursive Noise Statistic Based on MLE and EM
Tian, Y., Suwoyo, H., Wang, W., Mbemba, D., & Li, L. (2019) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-019-01044-8 · Cited by 18
Metadata
- Authors
- Dissanayake, M. W. M. G., Newman, P., Clark, S., Durrant-Whyte, H. F., & Csorba, M.
- Affiliations
- The University of Sydney (AU)
- ORCID
- Venue
- IEEE Transactions on Robotics and Automation
- Published
- 2001-06
- Volume
- 17
- Issue
- 3
- Pages
- 229-241
- ISSN
- 1042-296X (ISSN-L)
- OpenAlex
- W2143864104
- Cited by
- 2,661
- References
- 29
- Citations by year
- 2026: 37 · 2025: 48 · 2024: 52 · 2023: 63 · 2022: 83 · 2021: 88 · 2020: 109 · 2019: 109 · 2018: 120 · 2017: 108 · 2016: 116 · 2015: 115 · 2014: 137 · 2013: 119 · 2012: 140
- FWCI
- 158.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Robotics and Automation (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Keywords
- SLAM, autonomous vehicles, map building, vehicle localization, estimation theory, lower bounds, millimeter-wave radar, data association, map management
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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