Camera-IMU-based localization: Observability analysis and consistency improvement

Authors:
Joel A Hesch;Dimitrios G Kottas;Sean L Bowman;Stergios I Roumeliotis
Affiliations:
Google, Mountain View, CA, USA;Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA;Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA;Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA
Venue:
International Journal of Robotics Research
Year:
2014

Citing 16
Cited 0

Nonlinear control systems: an introduction (2nd ed.)

Nonlinear control systems: an introduction (2nd ed.)
Matrix analysis and applied linear algebra

Matrix analysis and applied linear algebra
Estimation with Applications to Tracking and Navigation

Estimation with Applications to Tracking and Navigation
Structure from Motion Causally Integrated Over Time

IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex Optimization

Convex Optimization
Real-time implementation of airborne inertial-SLAM

Robotics and Autonomous Systems
An iterative image registration technique with an application to stereo vision

IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Vision-aided inertial navigation on an uncertain map using a particle filter

ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Observability-based Rules for Designing Consistent EKF SLAM Estimators

International Journal of Robotics Research
Observability-based consistent EKF estimators for multi-robot cooperative localization

Autonomous Robots
Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration

International Journal of Robotics Research
Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

International Journal of Robotics Research
A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation

IEEE Transactions on Robotics
Vision and IMU Data Fusion: Closed-Form Solutions for Attitude, Speed, Absolute Scale, and Bias Determination

IEEE Transactions on Robotics
Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions

IEEE Transactions on Robotics
High-precision, consistent EKF-based visual-inertial odometry

International Journal of Robotics Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work investigates the relationship between system observability properties and estimator inconsistency for a Vision-aided Inertial Navigation System (VINS). In particular, first we introduce a new methodology for determining the unobservable directions of nonlinear systems by factorizing the observability matrix according to the observable and unobservable modes. Subsequently, we apply this method to the VINS nonlinear model and determine its unobservable directions analytically. We leverage our analysis to improve the accuracy and consistency of linearized estimators applied to VINS. Our key findings are evaluated through extensive simulations and experimental validation on real-world data, demonstrating the superior accuracy and consistency of the proposed VINS framework compared to standard approaches.