Yuan Huayu, Han Ke, Lou Boyang
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2024 Aug 12;24(16):5218. doi: 10.3390/s24165218.
In this paper, we proposed Mix-VIO, a monocular and binocular visual-inertial odometry, to address the issue where conventional visual front-end tracking often fails under dynamic lighting and image blur conditions. Mix-VIO adopts a hybrid tracking approach, combining traditional handcrafted tracking techniques with Deep Neural Network (DNN)-based feature extraction and matching pipelines. The system employs deep learning methods for rapid feature point detection, while integrating traditional optical flow methods and deep learning-based sparse feature matching methods to enhance front-end tracking performance under rapid camera motion and environmental illumination changes. In the back-end, we utilize sliding window and bundle adjustment (BA) techniques for local map optimization and pose estimation. We conduct extensive experimental validations of the hybrid feature extraction and matching methods, demonstrating the system's capability to maintain optimal tracking results under illumination changes and image blur.
在本文中，我们提出了Mix-VIO，一种单目和双目视觉惯性里程计，以解决传统视觉前端跟踪在动态光照和图像模糊条件下经常失败的问题。Mix-VIO采用了一种混合跟踪方法，将传统的手工跟踪技术与基于深度神经网络（DNN）的特征提取和匹配管道相结合。该系统采用深度学习方法进行快速特征点检测，同时集成传统光流方法和基于深度学习的稀疏特征匹配方法，以提高在快速相机运动和环境光照变化下的前端跟踪性能。在后端，我们利用滑动窗口和束调整（BA）技术进行局部地图优化和姿态估计。我们对混合特征提取和匹配方法进行了广泛的实验验证，证明了该系统在光照变化和图像模糊情况下保持最佳跟踪结果的能力。
