TY - GEN
T1 - On Robot Localization Safety for Fixed-Lag Smoothing
T2 - 2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
AU - Hafez, Osama Abdul
AU - Arana, Guillermo Duenas
AU - Chen, Yihe
AU - Joerger, Mathieu
AU - Spenko, Matthew
N1 - Funding Information:
*This work is supported by NSF Grant #1637899 1O. A. Hafez, 1G. D. Arana, 1Y. Chen, student members IEEE, and M. Spenko, senior member IEEE, are with the Mechanical, Materials, and Aerospace Engineering Department, Illinois Tech, Chicago, IL, USA [email protected] 2M. Joerger, member IEEE, is with the Dept. of Aerospace & Ocean Engineering, Virginia Tech, Blacksburg, VA, USA
Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - 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.
AB - 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.
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U2 - 10.1109/PLANS46316.2020.9110126
DO - 10.1109/PLANS46316.2020.9110126
M3 - Conference contribution
AN - SCOPUS:85087088117
T3 - 2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
SP - 306
EP - 317
BT - 2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 April 2020 through 23 April 2020
ER -