DVIO: Depth aided visual inertial odometry for RGBD sensors

TL;DR

This paper introduces DVIO, a depth-aided visual inertial odometry system that leverages RGBD sensors and IMUs, improving accuracy and efficiency for mobile device motion estimation in augmented reality applications.

Contribution

The paper presents a novel VIO system integrating depth measurements into nonlinear optimization, including methods for feature parameterization, time offset estimation, and a block-based marginalization approach.

Findings

DVIO outperforms state-of-the-art VIO systems in accuracy.

DVIO achieves faster processing times.

Depth integration enhances motion estimation robustness.

Abstract

In past few years we have observed an increase in the usage of RGBD sensors in mobile devices. These sensors provide a good estimate of the depth map for the camera frame, which can be used in numerous augmented reality applications. This paper presents a new visual inertial odometry (VIO) system, which uses measurements from a RGBD sensor and an inertial measurement unit (IMU) sensor for estimating the motion state of the mobile device. The resulting system is called the depth-aided VIO (DVIO) system. In this system we add the depth measurement as part of the nonlinear optimization process. Specifically, we propose methods to use the depth measurement using one-dimensional (1D) feature parameterization as well as three-dimensional (3D) feature parameterization. In addition, we propose to utilize the depth measurement for estimating time offset between the unsynchronized IMU and the RGBD sensors. Last but not least, we propose a novel block-based marginalization approach to speed up the marginalization processes and maintain the real-time performance of the overall system. Experimental results validate that the proposed DVIO system outperforms the other state-of-the-art VIO systems in terms of trajectory accuracy as well as processing time.