Map-Based Precision Vehicle Localization in Urban Environments
Open accessAbstract
Many urban navigation applications (e.g., autonomous navigation, driver assistance systems) can benefit greatly from localization with centimeter accuracy.Yet such accuracy cannot be achieved reliably with GPS-based inertial guidance systems, specifically in urban settings.We propose a technique for high-accuracy localization of moving vehicles that utilizes maps of urban environments.Our approach integrates GPS, IMU, wheel odometry, and LIDAR data acquired by an instrumented vehicle, to generate high-resolution environment maps.Offline relaxation techniques similar to recent SLAM methods [2, 10, 13, 14, 21, 30] are employed to bring the map into alignment at intersections and other regions of self-overlap.By reducing the final map to the flat road surface, imprints of other vehicles are removed.The result is a 2-D surface image of ground reflectivity in the infrared spectrum with 5cm pixel resolution.To localize a moving vehicle relative to these maps, we present a particle filter method for correlating LIDAR measurements with this map.As we show by experimentation, the resulting relative accuracies exceed that of conventional GPS-IMU-odometry-based methods by more than an order of magnitude.Specifically, we show that our algorithm is effective in urban environments, achieving reliable real-time localization with accuracy in the 10centimeter range.Experimental results are provided for localization in GPS-denied environments, during bad weather, and in dense traffic.The proposed approach has been used successfully for steering a car through narrow, dynamic urban roads.Figure 10.(Best viewed in color) Typical driving path during localization shown in green, and overlayed on a previously built map, acquired during 20 minutes of driving.For this and other paths, we find that the particle filter reliably localizes the vehicle.(a) GPS localization induces ≥1 meter of error.
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Running SLAM algorithms outdoors, the operational domain of ADSs, is less efficient than localization with a pre-built map.
“However, due to the high computational requirements and environmental challenges, running SLAM algorithms outdoors, which is the operational domain of ADSs, is less efficient than localization with a pre-built map [112].”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [112]
The Introduction (page not provided) describes difficult SLAM problems and the challenge of dynamic urban environments. Section III, “Online Localization” (page not provided), reports real-time localization using a previously built map. The paper does not compare the computational efficiency of running outdoor SLAM with map-based localization, so the claimed efficiency ranking is not established. (LLM, conf=0.82)
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- Authors
- Levinson, J., Montemerlo, M., & Thrun, S.
- Affiliations
- Stanford University (US)
- Venue
- Robotics: Science and Systems III
- Published
- 2007-06-27
- OpenAlex
- W179122090
- Cited by
- 477
- References
- 32
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 18 · 2023: 23 · 2022: 30 · 2021: 39 · 2020: 58 · 2019: 61 · 2018: 53 · 2017: 39 · 2016: 24 · 2015: 37 · 2014: 20 · 2013: 16 · 2012: 9
- FWCI
- 16.00
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Robotics: Science and Systems (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Keywords
- vehicle localization, urban navigation, map-based localization, LiDAR, particle filter, GPS-denied environments, SLAM, wheel odometry, inertial measurement unit, autonomous driving, heavy traffic, bad weather
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.
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