Real-Time RGBD Odometry for Fused-State Navigation Systems
This work addresses the need for accurate covariance estimation in RGBD odometry to improve localization in fused-state navigation systems, representing an incremental advancement.
The paper tackles the problem of providing real-time visual odometry estimates with covariance from RGBD images, which is essential for navigation systems but previously underexplored, and demonstrates its accuracy against motion capture ground truth.
This article describes an algorithm that provides visual odometry estimates from sequential pairs of RGBD images. The key contribution of this article on RGBD odometry is that it provides both an odometry estimate and a covariance for the odometry parameters in real-time via a representative covariance matrix. Accurate, real-time parameter covariance is essential to effectively fuse odometry measurements into most navigation systems. To date, this topic has seen little treatment in research which limits the impact existing RGBD odometry approaches have for localization in these systems. Covariance estimates are obtained via a statistical perturbation approach motivated by real-world models of RGBD sensor measurement noise. Results discuss the accuracy of our RGBD odometry approach with respect to ground truth obtained from a motion capture system and characterizes the suitability of this approach for estimating the true RGBD odometry parameter uncertainty.