Observability Analysis and Performance Evaluation of EKF-Based Visual-Inertial Odometry with Online Intrinsic Camera Parameter Calibration

Shu Hua Tsao, Shau Shiun Jan

Research output: Contribution to journalArticle

Abstract

In this paper, we focus on the problem of online intrinsic camera parameter calibration for a visual-inertial system. Imprecise intrinsic camera parameters will result in unreliable pose estimation or even cause estimator divergence. Specifically, we present a nonlinear observability analysis of the system and prove that there are four unobservable directions spanning the right nullspace of the observability matrix, i.e., the rotation about the gravity vector and the positions in the global frame. We propose an extended Kalman filter-based visual-inertial odometry method for calibrating intrinsic camera parameters while estimating the pose simultaneously. The observability properties and the performance of the estimator are validated using both the simulated and real-world datasets.

Original languageEnglish
Article number8576618
Pages (from-to)2695-2703
Number of pages9
JournalIEEE Sensors Journal
Volume19
Issue number7
DOIs
Publication statusPublished - 2019 Apr 1

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering

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