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Sensors 2018, 18(8), 2740; https://doi.org/10.3390/s18082740

A New Approach to Unwanted-Object Detection in GNSS/LiDAR-Based Navigation

1
College of Aerospace & Mechanical Engineering, The University of Arizona, Tucson, AZ 85721, USA
2
Armour College of Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
*
Author to whom correspondence should be addressed.
Received: 21 June 2018 / Revised: 15 August 2018 / Accepted: 16 August 2018 / Published: 20 August 2018
(This article belongs to the Special Issue GNSS and Fusion with Other Sensors)
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Abstract

In this paper, we develop new methods to assess safety risks of an integrated GNSS/LiDAR navigation system for highly automated vehicle (HAV) applications. LiDAR navigation requires feature extraction (FE) and data association (DA). In prior work, we established an FE and DA risk prediction algorithm assuming that the set of extracted features matched the set of mapped landmarks. This paper addresses these limiting assumptions by incorporating a Kalman filter innovation-based test to detect unwanted object (UO). UO include unmapped, moving, and wrongly excluded landmarks. An integrity risk bound is derived to account for the risk of not detecting UO. Direct simulations and preliminary testing help quantify the impact on integrity and continuity of UO monitoring in an example GNSS/LiDAR implementation. View Full-Text
Keywords: navigation; safety; GNSS; LiDAR; detection; integrity monitoring; autonomous cars navigation; safety; GNSS; LiDAR; detection; integrity monitoring; autonomous cars
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Joerger, M.; Duenas Arana, G.; Spenko, M.; Pervan, B. A New Approach to Unwanted-Object Detection in GNSS/LiDAR-Based Navigation. Sensors 2018, 18, 2740.

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