Quantifying safety of laser-based navigation

Mathieu Joerger; Boris Pervan

doi:10.1109/TAES.2018.2850381

Quantifying safety of laser-based navigation

Mathieu Joerger
, Boris Pervan

Aerospace and Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

In this paper, a new safety risk evaluation method is developed, simulated, and tested for laser-based navigation algorithms using feature extraction (FE) and data association (DA). First, at FE, we establish a probabilistic measure of separation between features to quantify the sensor's ability to distinguish landmarks. Then, an innovation-based DA process is designed to evaluate the impact on integrity risk of incorrect associations, while considering all potential measurement permutations. The algorithm is analyzed and tested in a structured environment.

Original language	English (US)
Article number	8395344
Pages (from-to)	273-288
Number of pages	16
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	55
Issue number	1
DOIs	https://doi.org/10.1109/TAES.2018.2850381
State	Published - Feb 2019

Keywords

Autonomous vehicle
data association (DA)
integrity
navigation
safety

ASJC Scopus subject areas

Aerospace Engineering
Electrical and Electronic Engineering

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10.1109/TAES.2018.2850381

Cite this

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abstract = "In this paper, a new safety risk evaluation method is developed, simulated, and tested for laser-based navigation algorithms using feature extraction (FE) and data association (DA). First, at FE, we establish a probabilistic measure of separation between features to quantify the sensor's ability to distinguish landmarks. Then, an innovation-based DA process is designed to evaluate the impact on integrity risk of incorrect associations, while considering all potential measurement permutations. The algorithm is analyzed and tested in a structured environment.",

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AB - In this paper, a new safety risk evaluation method is developed, simulated, and tested for laser-based navigation algorithms using feature extraction (FE) and data association (DA). First, at FE, we establish a probabilistic measure of separation between features to quantify the sensor's ability to distinguish landmarks. Then, an innovation-based DA process is designed to evaluate the impact on integrity risk of incorrect associations, while considering all potential measurement permutations. The algorithm is analyzed and tested in a structured environment.

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KW - data association (DA)

KW - integrity

KW - navigation

KW - safety

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Quantifying safety of laser-based navigation

Abstract

Keywords

ASJC Scopus subject areas

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