JOURNAL ARTICLE

Recursive Integrity Monitoring for Mobile Robot Localization Safety

Abstract

This paper presents a new methodology to quantify robot localization safety by evaluating integrity risk, a performance metric widely used in open-sky aviation applications that has been recently extended to mobile ground robots. Here, a robot is localized by feeding relative measurements to mapped landmarks into an Extended Kalman Filter while a sequence of innovations is evaluated for fault detection. The main contribution is the derivation of a sequential chi-squared integrity monitoring methodology that maintains constant computation requirements by employing a preceding time window and, at the same time, is robust against faults occurring prior to the window. Additionally, no assumptions are made on either the nature or shape of the faults because safety is evaluated under the worst possible combination of sensor faults.

Keywords:
Mobile robot Sliding window protocol Computer science Real-time computing Metric (unit) Robot Computation Kalman filter Extended Kalman filter Fault detection and isolation Fault (geology) Window (computing) Artificial intelligence Algorithm Engineering

Metrics

28
Cited By
2.61
FWCI (Field Weighted Citation Impact)
26
Refs
0.91
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Target Tracking and Data Fusion in Sensor Networks
Physical Sciences →  Computer Science →  Artificial Intelligence
Robotics and Sensor-Based Localization
Physical Sciences →  Engineering →  Aerospace Engineering
Distributed Sensor Networks and Detection Algorithms
Physical Sciences →  Computer Science →  Computer Networks and Communications

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