Abstract
Localization safety, or integrity risk, is the probability of undetected localization failures and a common aviation performance metric used to verify a minimum accuracy requirement. As autonomous robots become more common, applying integrity risk metrics will be necessary to verify localization performance. This letter introduces a new method, solution separation, to quantify landmark-based mobile robot localization safety for fixed-lag smoothing estimators and compares it's computation time and fault detection capabilities to a chi-squared integrity monitoring method. Results show that solution separation is more computationally efficient and results in a tighter upper-bound on integrity risk when few measurements are included, which makes it the method of choice for lightweight, safety-critical applications such as UAVs. Conversely, chi-squared requires more computing resources but performs better when more measurements are included, making the method more appropriate for high performance computing platforms such as autonomous vehicles.
| Original language | English (US) |
|---|---|
| Article number | 9006956 |
| Pages (from-to) | 3182-3189 |
| Number of pages | 8 |
| Journal | IEEE Robotics and Automation Letters |
| Volume | 5 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 2020 |
Keywords
- Localization
- autonomous vehicle navigation
- performance evaluation and benchmarking
- probability and statistical methods
- robot safety
ASJC Scopus subject areas
- Control and Systems Engineering
- Biomedical Engineering
- Human-Computer Interaction
- Mechanical Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications
- Control and Optimization
- Artificial Intelligence
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