DVIO: Depth-Aided Visual Inertial Odometry for RGBD Sensors

Abhishek Tyagi, Yangwen Liang, Shuangquan Wang, Dongwoon Bai
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引用次数: 4

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.
RGBD传感器的深度辅助视觉惯性里程计
在过去的几年里,我们观察到RGBD传感器在移动设备中的使用有所增加。这些传感器为相机帧提供了一个很好的深度图估计,可用于许多增强现实应用。本文提出了一种新的视觉惯性里程计(VIO)系统,该系统利用RGBD传感器和惯性测量单元(IMU)传感器的测量值来估计移动设备的运动状态。由此产生的系统称为深度辅助VIO (DVIO)系统。在该系统中,我们将深度测量作为非线性优化过程的一部分。具体来说,我们提出了使用一维(1D)特征参数化和三维(3D)特征参数化来使用深度测量的方法。此外,我们建议利用深度测量来估计非同步IMU和RGBD传感器之间的时间偏移。最后,我们提出了一种新的基于块的边缘化方法,以加快边缘化过程并保持整个系统的实时性能。实验结果表明,所提出的DVIO系统在弹道精度和处理时间方面优于其他先进的VIO系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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