采用双视觉里程计的SLAM位姿图优化方法

A SLAM Pose Graph Optimization Method Using Dual Visual Odometry

后端轨迹优化是视觉同步定位与建图系统的重要组成部分,可以显著地提高定位精度.然而,现有的基于捆集约束法的优化方法在大场景中计算量大,并且无法应用于端到端视觉里程计.针对这个问题,提出了一种在前端采用2个视觉里程计的后端通用位姿图优化方法,可以应用于端到端视觉里程计.该方法采用一个高速低精度的端到端视觉里程计以高频率运行,同时一个低速高精度的视觉里程计以低频率运行,局部优化通过2个里程计提供的约束条件使用高斯-牛顿法迭代优化;在全局优化中基于关键帧进行场景匹配与局部优化同时进行.实验证明,应用该优化方法的同步定位与建图系统可以在KITTI数据集上实时运行,相较于2个视觉里程计都取得了精度上的较大提升,并且对比现今开源的几种应用后端轨迹优化的著名同步定位与建图方法,在轨迹误差、绝对轨迹误差、旋转误差和相对位姿误差上均取得了较低的误差,兼顾了传统方法精度的优势和端到端方法速度上的优点.除此以外,该优化方法还可以适用于其他更多的视觉里程计.

Backend trajectory optimization is an important part of the visual simultaneous localization and map-ping system,which can significantly improve localization accuracy.However,the existing optimization methods based on the bundle adjustment have a large amount of calculation in large scenes and cannot be applied to end-to-end visual odometries.To solve this problem,a universal backend pose graph optimization algorithm with two visual odometries at the front end is proposed,which can be applied to end-to-end visual odometries.This method uses a high-speed but low-precision end-to-end visual odometry to run at high frequency,while a low-speed but high-precision visual odometry runs at a low frequency.Local optimization uses Gauss-Newton method iterative optimization through the constraints provided by two odometries.Global optimization is per-formed simultaneously which based on key frames scene matching.Experiments show that the simultaneous lo-calization and mapping system which apply this optimization method can run in real-time on the KITTI dataset.Compared with the two visual odometries,the accuracy has been greatly improved.And compared with several well-known open source simultaneous localization and mapping methods that apply backend trajectory opti-mization,low errors have been achieved in trajectory error,absolute translational error,rotation error and rela-tive pose error,taking into account the advantages of the accuracy of traditional methods and the advantages of high speed end-to-end methods.In addition,the optimization framework can also be applied to other more visual odometries.