快速提取特征点的视觉SLAM室内稠密建图研究

Research on visual SLAM indoor dense mapping for quickly extracting feature points

在视觉SLAM(simultaneous localization and mapping)中,特征点的提取是影响全局即时定位与地图构建效率的重要因素。对视觉ORB-SLAM2算法进行研究,提出一种自适应网格划分的方法优化特征点提取的效率,通过对图像金字塔层进行网格划分,提高特征点提取的速度。在TUM数据集上进行了单目(MONO)和RGB-D测试,结果表明,在平均每帧特征点提取时间提高8%~10%,绝对轨迹误差减少5%以上。在自适应网格算法中加入RGB-D稠密点云构建线程,采用外点去除滤波和体素网格滤波减小点云规模,实现了稠密建图。在TUM数据集上,该方法的室内稠密建图效果显著。

In visual SLAM,the extraction of feature points is an important factor that affects global real-time localization and mapping efficiency.This paper studies visual ORB-SLAM2 algorithm and proposes an adaptive meshing method to optimize the efficiency of feature point extraction.Meshing the image pyramid layer improves the speed of feature point extraction.This study conducts monocular(MONO)and RGB-D tests on the TUM dataset.The results show the average feature point extraction time per frame increases by 8%-10%while the absolute trajectory error is down by over 5%respectively.Meanwhile,the RGB-D dense point cloud construction thread is added to the adaptive grid algorithm,and the outlier removal filter and voxel grid filter are employed to reduce the point cloud scale and realize dense mapping.The experimental results on the TUM dataset show the method is highly effective in indoor dense mapping.