On Robot Localization Safety for Fixed-Lag Smoothing: Quantifying the Risk of Misassociation

Osama Abdul Hafez, Guillermo Duenas Arana, Yihe Chen, Mathieu Joerger, and Matthew Spenko

Abstract: Monitoring localization safety will be necessary to certify the performance of robots that operate in life-critical applications, such as autonomous passenger vehicles or delivery drones because many current localization safety methods do not account for the risk of undetected sensor faults. One type of fault, misassociation, occurs when a feature extracted from a mapped landmark is associated to a non-corresponding landmark and is a common source of error in feature-based navigation applications. This paper accounts for the probability of misassociation when quantifying landmark-based mobile robot localization safety for fixed-lag smoothing estimators. We derive a mobile robot localization safety bound and evaluate it using simulations and experimental data in an urban environment. Results show that localization safety suffers when landmark density is relatively low such that there are not enough landmarks to adequately localize and when landmark density is relatively high because of the high risk of feature misassociation.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 306 - 317
Cite this article: Hafez, Osama Abdul, Arana, Guillermo Duenas, Chen, Yihe, Joerger, Mathieu, Spenko, Matthew, "On Robot Localization Safety for Fixed-Lag Smoothing: Quantifying the Risk of Misassociation," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 306-317.
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