Vehicle Localization Based on Visual Lane Marking and Topological Map Matching

Rabbia Asghar; Mario Garzon; Jerome Lussereau; Christian Laugier

doi:10.1109/ICRA40945.2020.9197543

Vehicle Localization Based on Visual Lane Marking and Topological Map Matching

Rabbia Asghar, Mario Garzon, Jerome Lussereau, Christian Laugier

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

10 Citations (Scopus)

Abstract

Accurate and reliable localization is crucial to autonomous vehicle navigation and driver assistance systems. This paper presents a novel approach for online vehicle localization in a digital map. Two distinct map matching algorithms are proposed: i) Iterative Closest Point (ICP) based lane level map matching is performed with visual lane tracker and grid map ii) decision-rule based approach is used to perform topological map matching. Results of both the map matching algorithms are fused together with GPS and dead reckoning using Extended Kalman Filter to estimate vehicle's pose relative to the map. The proposed approach has been validated on real life conditions on an equipped vehicle. Detailed analysis of the experimental results show improved localization using the two aforementioned map matching algorithms.

Original language	English
Title of host publication	2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Publisher	IEEE
Pages	258-264
ISBN (Electronic)	9781728173955
DOIs	https://doi.org/10.1109/ICRA40945.2020.9197543
Publication status	Published - 2020
Externally published	Yes
Event	2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France Duration: 31 May 2020 → 31 Aug 2020

Conference

Conference	2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Country/Territory	France
City	Paris
Period	31/05/20 → 31/08/20

Keywords

Autonomous Vehicles
Lane Level Matching
Map Relative Localization
Topological Map Matching

Access to Document

10.1109/ICRA40945.2020.9197543

Cite this

@inproceedings{6d644602068345f0a9bad8c83cc8e0ce,

title = "Vehicle Localization Based on Visual Lane Marking and Topological Map Matching",

abstract = "Accurate and reliable localization is crucial to autonomous vehicle navigation and driver assistance systems. This paper presents a novel approach for online vehicle localization in a digital map. Two distinct map matching algorithms are proposed: i) Iterative Closest Point (ICP) based lane level map matching is performed with visual lane tracker and grid map ii) decision-rule based approach is used to perform topological map matching. Results of both the map matching algorithms are fused together with GPS and dead reckoning using Extended Kalman Filter to estimate vehicle's pose relative to the map. The proposed approach has been validated on real life conditions on an equipped vehicle. Detailed analysis of the experimental results show improved localization using the two aforementioned map matching algorithms.",

keywords = "Autonomous Vehicles, Lane Level Matching, Map Relative Localization, Topological Map Matching",

author = "Rabbia Asghar and Mario Garzon and Jerome Lussereau and Christian Laugier",

year = "2020",

doi = "10.1109/ICRA40945.2020.9197543",

language = "English",

pages = "258--264",

booktitle = "2020 IEEE International Conference on Robotics and Automation, ICRA 2020",

publisher = "IEEE",

address = "United States",

note = "2020 IEEE International Conference on Robotics and Automation, ICRA 2020 ; Conference date: 31-05-2020 Through 31-08-2020",

}

Asghar, R, Garzon, M, Lussereau, J & Laugier, C 2020, Vehicle Localization Based on Visual Lane Marking and Topological Map Matching. in 2020 IEEE International Conference on Robotics and Automation, ICRA 2020. IEEE, pp. 258-264, 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, 31/05/20. https://doi.org/10.1109/ICRA40945.2020.9197543

TY - GEN

T1 - Vehicle Localization Based on Visual Lane Marking and Topological Map Matching

AU - Asghar, Rabbia

AU - Garzon, Mario

AU - Lussereau, Jerome

AU - Laugier, Christian

PY - 2020

Y1 - 2020

N2 - Accurate and reliable localization is crucial to autonomous vehicle navigation and driver assistance systems. This paper presents a novel approach for online vehicle localization in a digital map. Two distinct map matching algorithms are proposed: i) Iterative Closest Point (ICP) based lane level map matching is performed with visual lane tracker and grid map ii) decision-rule based approach is used to perform topological map matching. Results of both the map matching algorithms are fused together with GPS and dead reckoning using Extended Kalman Filter to estimate vehicle's pose relative to the map. The proposed approach has been validated on real life conditions on an equipped vehicle. Detailed analysis of the experimental results show improved localization using the two aforementioned map matching algorithms.

AB - Accurate and reliable localization is crucial to autonomous vehicle navigation and driver assistance systems. This paper presents a novel approach for online vehicle localization in a digital map. Two distinct map matching algorithms are proposed: i) Iterative Closest Point (ICP) based lane level map matching is performed with visual lane tracker and grid map ii) decision-rule based approach is used to perform topological map matching. Results of both the map matching algorithms are fused together with GPS and dead reckoning using Extended Kalman Filter to estimate vehicle's pose relative to the map. The proposed approach has been validated on real life conditions on an equipped vehicle. Detailed analysis of the experimental results show improved localization using the two aforementioned map matching algorithms.

KW - Autonomous Vehicles

KW - Lane Level Matching

KW - Map Relative Localization

KW - Topological Map Matching

UR - http://www.scopus.com/inward/record.url?scp=85092737405&partnerID=8YFLogxK

U2 - 10.1109/ICRA40945.2020.9197543

DO - 10.1109/ICRA40945.2020.9197543

M3 - Conference contribution

AN - SCOPUS:85092737405

SP - 258

EP - 264

BT - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020

PB - IEEE

T2 - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020

Y2 - 31 May 2020 through 31 August 2020

ER -

Vehicle Localization Based on Visual Lane Marking and Topological Map Matching

Abstract

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Keywords

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