多传感器信息融合的移动机器人定位算法研究

摘要：

为有效降低使用单一传感器进行移动机器人定位时的不确定误差， 提高机器人定位与建图的准确性和鲁棒性， 提出了一种多传感器信息融合的移动机器人定位算法。 基于激光RBPF-SLAM算法实现机器人同时定位与路标地图构建， 运用图优化理论约束优化蒙特卡洛定位的位姿估计结果； 通过双目视觉重建环境的三维点特征， 针对视觉信息处理计算量大、 跟踪精度不高的问题， 研究改进基于ORB的特征点提取与四边形闭环匹配算法； 利用因子图模型对激光RBPF-SLAM定位和双目视觉定位进行最大后验概率准则下的信息融合。 仿真和实验结果表明通过上述方法可以得到比传统RBPF-SLAM算法及一般改进算法更高的定位精度， 验证了所提方法的有效性和实用性。

Abstract： In order to effectively reduce the uncertainty error of mobile robot localization with a single sensor and improve the accuracy and robustness of robot localization and mapping, a mobile robot localization algorithm based on multi-sensor information fusion (MSIF) was proposed. In this paper, simultaneous localization and mapping (SLAM) was realized on the basis of laser Rao-Blackwellized particle filter (RBPF)-SLAM algorithm and graph-based optimization theory was used to constrain and optimize the pose estimation results of Monte Carlo localization. The feature point extraction and quadrilateral closed loop matching algorithm based on oriented FAST and rotated BRIEF (ORB) were improved aiming at the problems of generous calculation and low tracking accuracy in visual information processing by means of the three-dimensional (3D) point feature in binocular visual reconstruction environment. Factor graph model was used for the information fusion under the maximum posterior probability criterion for laser RBPF-SLAM localization and binocular visual localization. The results of simulation and experiment indicate that localization accuracy of the above-mentioned method is higher than that of traditional RBPF-SLAM algorithm and general improved algorithms, and the effectiveness and usefulness of the proposed method  are verified.