As an important research field of mobile robot, simultaneous localization and mapping technology is the core technology to realize intelligent autonomous mobile robot. Aiming at the problems of low positioning accuracy of Lidar (light detection and ranging) simultaneous localization and mapping with nonlinear and non-Gaussian noise characteristics, this article presents a mobile robot simultaneous localization and mapping method that combines Lidar and inertial measurement unit to set up a multi-sensor integrated system and uses a rank Kalman filtering to estimate the robot motion trajectory through inertial measurement unit and Lidar observations. Rank Kalman filtering is similar to the Gaussian deterministic point sampling filtering algorithm in structure, but it does not need to meet the assumptions of Gaussian distribution. It completely calculates the sampling points and the sampling points weights based on the correlation principle of rank statistics. It is suitable for nonlinear and non-Gaussian systems. With multiple experimental tests of small-scale arc trajectories, we can see that compared with the alone Lidar simultaneous localization and mapping algorithm, the new algorithm reduces the mean error of the indoor mobile robot in the X direction from 0.0928 m to 0.0451 m, with an improved accuracy rate of 46.39%, and the mean error in the Y direction from 0.0772 m to 0.0405 m, which improves the accuracy rate of 48.40%. Compared with the extended Kalman filter fusion algorithm, the new algorithm reduces the mean error of the indoor mobile robot in the X direction from 0.0597 m to 0.0451 m, with an improved accuracy rate of 24.46%, and the mean error in the Y direction from 0.0537 m to 0.0405 m, which improves the accuracy rate of 24.58%. Finally, we also tested on a large-scale rectangular trajectory, compared with the extended Kalman filter algorithm, rank Kalman filtering improves the accuracy of 23.84% and 25.26% in the X and Y directions, respectively, it is verified that the accuracy of the algorithm proposed in this article has been improved.

作为移动机器人的重要研究领域，同步定位与制图技术是实现智能自主移动机器人的核心技术。针对具有非线性和非高斯噪声特性的激光雷达同时定位与制图定位精度低的问题，提出了一种结合了激光雷达与惯性测量单元进行设置的移动机器人同时定位与制图方法。多传感器集成系统，并使用秩卡尔曼滤波通过惯性测量单元和激光雷达观测来估计机器人的运动轨迹。秩卡尔曼滤波在结构上与高斯确定性点采样滤波算法相似，但是不需要满足高斯分布的假设。它根据秩统计的相关原理，完全计算出采样点和采样点权重。它适用于非线性和非高斯系统。通过对小规模弧形轨迹的多次实验测试，我们可以看到，与单独的Lidar同时定位和映射算法相比，该新算法减少了室内移动机器人在运动中的平均误差。X方向从0.0928 m到0.0451 m，提高了46.39％的准确率，Y方向的平均误差从0.0772 m到0.0405 m，提高了48.40％的准确率。与扩展的卡尔曼滤波融合算法相比，新算法将室内移动机器人在X方向的平均误差从0.0597 m减小到0.0451 m，提高了24.46％的准确率，在Y方向的平均误差也提高了24.46％。 0.0537 m至0.0405 m，提高了准确率24.58％。最后，我们还对大型矩形轨迹进行了测试，与扩展卡尔曼滤波算法相比，秩卡尔曼滤波将X和Y的精度提高了23.84％和25.26％ 分别验证了本文提出的算法的准确性。