Ye Pei-chu, Li Dong, Zhang Yun. Direct Sparse Visual Odometer Based on Enhanced Stereo-Camera Constraints[J]. 2021, 38(4): 65-70.DOI: 10.12052/gdutxb.200138.
Direct Sparse Visual Odometer Based on Enhanced Stereo-Camera Constraints
提出了一种基于双目强约束的直接稀疏视觉里程计系统。基于直接法的即时定位与地图构建(Simultaneous Localization and Mapping
SLAM)系统直接对图像像素构建光度误差优化函数
无需提取特征点
克服了基于特征点法的SLAM系统在弱纹理场景下不鲁棒的缺陷
并且在前端跟踪阶段效率更高。提出一种快速、准确的双目初始化方法
结合三角化不确定性为不同类型的点赋予不同的深度范围
加速深度滤波器的收敛。同时
在运动估计阶段引入双目约束
使得该系统在绝对尺度上的定位更加准确。通过在公开的KITTI数据集11个序列上进行实验
实验结果表明所提出的算法在定位精度上明显优于同样采用直接法的Stereo Large Scale Direct SLAM(LSD-SLAM2)和Stereo DSO
并达到与基于特征点法的ORB-SLAM3相近的水平
为直接法SLAM提供一种更优的定位方案。
Abstract
A new direct SLAM (Simultaneous Localization And Mapping) system with enhanced stereo-camera constrains based on Stereo Direct Sparse Odometry (Stereo DSO) is presented. As a direct SLAM method
any image pixel with sufficient intensity gradient can be utilized
which makes it robust even in featureless areas. Two-stage checking combining SAD (Sum of Absolute Differences) with NCC (Normalized Cross Correlation) matching method is used for stereo matching and triangulated uncertainty concerned to accelerate the convergence of depth filters. To estimate the accurate scale of the environment
static stereo constrains are added to the tracking module. Our evaluation on KITTI demonstrates that the proposed system achieves the better performance than the state of the art direct SLAM systems such as LSD-SLAM2 and Stereo DSO
and achieves the comparable performance with ORB-SLAM3 which is the state of the art feature SLAM. The proposal provides mobile robots with a new direct SLAM system to explore the environment more precisely and robustly.
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references
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