A new approach to unwanted-object detection in GNSS/LiDAR-based navigation

Mathieu Joerger, Guillermo Duenas Arana, Matthew Spenko, Boris Pervan

Research output: Contribution to journalArticle

6 Scopus citations

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.

Original languageEnglish (US)
Article number2740
JournalSensors (Switzerland)
Volume18
Issue number8
DOIs
StatePublished - Aug 20 2018

Keywords

  • Autonomous cars
  • Detection
  • GNSS
  • Integrity monitoring
  • LiDAR
  • Navigation
  • Safety

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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