Visual-Inertial Odometry with Online Calibration of Velocity-Control Based Kinematic Motion Models

TL;DR

This paper introduces a novel stereo camera-based visual-inertial odometry system that online calibrates a velocity-control kinematic model without requiring wheel encoders, improving accuracy and enabling better motion prediction for wheeled robots.

Contribution

It presents an online calibration method for velocity-control kinematic models in VIO systems that does not depend on wheel encoders, enhancing accuracy and applicability.

Findings

Improved VIO accuracy with the integrated motion model.

Effective online calibration of the kinematic model.

Enhanced motion prediction capabilities.

Abstract

Visual-inertial odometry (VIO) is an important technology for autonomous robots with power and payload constraints. In this paper, we propose a novel approach for VIO with stereo cameras which integrates and calibrates the velocity-control based kinematic motion model of wheeled mobile robots online. Including such a motion model can help to improve the accuracy of VIO. Compared to several previous approaches proposed to integrate wheel odometer measurements for this purpose, our method does not require wheel encoders and can be applied when the robot motion can be modeled with velocity-control based kinematic motion model. We use radial basis function (RBF) kernels to compensate for the time delay and deviations between control commands and actual robot motion. The motion model is calibrated online by the VIO system and can be used as a forward model for motion control and planning. We evaluate our approach with data obtained in variously sized indoor environments, demonstrate improvements over a pure VIO method, and evaluate the prediction accuracy of the online calibrated model.