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Updated: Feb 6, 2026

Practical Methodology of Cognitive Tasks Within a Navigational Assessment
Published on: June 1, 2015
A New Approach to Unwanted-Object Detection in GNSS/LiDAR-Based Navigation
Mathieu Joerger1, Guillermo Duenas Arana2, Matthew Spenko3
1College of Aerospace & Mechanical Engineering, The University of Arizona, Tucson, AZ 85721, USA. joerger@email.arizona.edu.
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.
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