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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FastSLAM recursively approximates the entire posterior distribution over robot pose and landmark locations, yet scales exponentially with the number of landmarks on the map.
“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]
FastSLAM uses a hybrid technique integrating Particle Filter and EKF, making it popular for higher data accuracy.
“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]
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- 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 › Engineering › Aerospace Engineering › 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
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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.
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