TY - GEN
T1 - Local Nearest Neighbor Integrity Risk Evaluation for Robot Navigation
AU - Duenasarana, Guillermo
AU - Joerger, Mathieu
AU - Spenko, Matthew
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - This paper describes the design of a new integrity risk prediction/monitoring methodology for robot localization that uses feature extraction and data association algorithms. The work specifically addresses incorrect association faults when employing a local nearest neighbor data association algorithm. This approach is more efficient and easier to implement than previous work. The methodology is tested in simulation, showing that the computed upper bound on integrity risk is a performance metric capable of providing warnings when the safety of the system cannot be guaranteed.
AB - This paper describes the design of a new integrity risk prediction/monitoring methodology for robot localization that uses feature extraction and data association algorithms. The work specifically addresses incorrect association faults when employing a local nearest neighbor data association algorithm. This approach is more efficient and easier to implement than previous work. The methodology is tested in simulation, showing that the computed upper bound on integrity risk is a performance metric capable of providing warnings when the safety of the system cannot be guaranteed.
UR - https://www.scopus.com/pages/publications/85063145546
UR - https://www.scopus.com/pages/publications/85063145546#tab=citedBy
U2 - 10.1109/ICRA.2018.8460762
DO - 10.1109/ICRA.2018.8460762
M3 - Conference contribution
AN - SCOPUS:85063145546
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 2328
EP - 2333
BT - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018
Y2 - 21 May 2018 through 25 May 2018
ER -