基于单目/IMU/里程计融合的SLAM算法

doi:10.12382/bgxb.2022.0240

摘要/Abstract

摘要： 常见的单目视觉-惯性SLAM算法，应用于以平面运动为主的轮式机器人时，由于存在额外不可观测度等原因常会导致导航定位精度下降。为解决该问题，提出一种能提高定位精度的视觉/IMU/里程计紧耦合的SLAM算法。在视觉前端部分，改进了原始图像金字塔LK光流法，将陀螺仪的旋转信息和里程计的平移信息作为先验，进行了可减少计算量的光流初值计算过程优化；引入车轮里程计信息，推导了IMU/里程计预积分；将里程计约束加入初始化过程和后端非线性优化中，实现视觉、IMU、里程计信息的充分融合利用。开源数据集测试和小车实验结果表明，新算法光流迭代次数减少约32.5%，定位误差均值相比VINS-Mono减少约40%。

关键词: 轮式机器人, 单目相机, 惯性测量单元, 光流法, 里程计, 导航

Abstract: It is common for navigation and positioning accuracy to be reduced when the monocular vision-inertial SLAM algorithm is applied to planar wheeled robots due to additional unobservability. To solve this problem, a tightly-coupled Visual/IMU/Odometer SLAM algorithm is proposed to improve localization accuracy. First, in the visual front-end part, the original image pyramid LK optical flow method is improved, and the rotation information of the gyroscope and the translation information from the odometer are used as priors to optimize the initial optical flow calculation process, thus reducing the calculation amount. Second, IMU/Odometer pre-integral is derived by introducing the wheel odometer information. Finally, odometer constraints are added into the initialization process and back-end nonlinear optimization to realize that vision, IMU, and odometer information are fully integrated. The results of the open-source data set test and car experiment show that the optical flow iteration time of the new algorithm is reduced by about 32.5%, and the average positioning error reduced by about 40% compared with that of VINS-Mono.

Key words: wheeledrobot, monocularcamera, inertialmeasurementunit, opticalflowmethod, odometry, navigation

中图分类号:

TP242.3

张福斌，张炳烁，杨玉帅. 基于单目/IMU/里程计融合的SLAM算法[J]. 兵工学报, 2022, 43(11): 2810-2817.

ZHANG Fubing, ZHANG Bingshuo, YANG Yushuai. SLAM Algorithm Based on Monocular/IMU/Odometer Fusion[J]. Acta Armamentarii, 2022, 43(11): 2810-2817.

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