基于PROSAC错配剔除算法的视觉与惯性融合里程计研究

Proceedings of the 2019 International Conference on Robotics Systems and Vehicle Technology - RSVT '19 Pub Date : 2019-10-16 DOI:10.1145/3366715.3366725

Lingxing Deng, Xun Li, Yanduo Zhang

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引用次数: 2

摘要

提出了一种基于渐进式采样共识(PROSAC)的单目视觉与惯性导航相结合的定位方法，重点解决了低成本设备在未知环境下的自定位问题。本文采用PROSAC算法，结合惯性测量单元(IMU)，通过预积分计算相机的相对运动距离，辅助定位。将PROSAC失配剔除算法加入到视觉惯导测距中，并与传统的viorb、VINS等方法在EuRoC数据集上的性能进行了比较。验证了该方法的有效性。平均误差为0.069m，分别比两种算法低11.1%和7.7%。

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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Research on Visual and Inertia Fusion Odometry Based on PROSAC Mismatched Culling Algorithm

A method based on Progressive Sampling Consensus(PROSAC) combining Monocular visual and inertial navigation is proposed for localization, which focuses on solving the problem of self-positioning of low-cost devices in an unknown environment. This paper used the PROSAC algorithm, and the Inertial Measurement Unit (IMU) to calculate the relative motion distance of the camera by pre-integration to assist the positioning. the PROSAC mismatch culling algorithm is added to the visual inertial navigation odometry and compared its performance with traditional methods-VIORB, VINS in the EuRoC data sets. Proving the effectiveness of the method. The average error is 0.069m, which is 11.1% and 7.7% lower than the two algorithms.

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Proceedings of the 2019 International Conference on Robotics Systems and Vehicle Technology - RSVT '19

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