Novel indoor positioning algorithm based on Lidar/inertial measurement unit integrated system
Novel indoor positioning algorithm based on Lidar/inertial measurement unit integrated system
复制标题
基于激光雷达/惯性测量单元集成系统的新型室内定位算法
DOI：
10.1177/1729881421999923
复制
发表时间：
2021-03-01
影响因子：
2.3
通讯作者：
Xiong, Jian
中科院分区：
文献类型：
--
作者：
Jiang, Ping;Chen, Liang;Xiong, Jian
同时定位与地图构建技术作为移动的机器人的一个重要研究领域，是实现智能自主移动的机器人的核心技术。针对激光雷达定位精度低的问题（光检测和测距）具有非线性和非高斯噪声特性的同时定位和映射，摘要提出了一种移动的机器人同时定位和地图绘制方法，该方法结合激光雷达和惯性测量单元，传感器集成系统，并使用秩卡尔曼滤波估计机器人的运动轨迹，通过惯性测量单元和激光雷达观测。秩卡尔曼滤波在结构上类似于高斯确定性点采样滤波算法，但它不需要满足高斯分布的假设。该方法基于秩统计量的相关性原理，完整地计算出样本点和样本点的权重。它适用于非线性和非高斯系统。通过多次小范围圆弧轨迹的实验测试可以看出，与单独的激光雷达同时定位与建图算法相比，新算法将室内移动的机器人在X方向的平均误差从0.0928 m降低到0.0451 m，准确率提高了46.39%，Y方向平均误差为0.0772 ~ 0.0405 m，提高了48.40%的精度。与扩展卡尔曼滤波融合算法相比，新算法使室内移动的机器人在X方向的平均误差从0.0597 m降低到0.0451 m，准确率提高了24.46%，在Y方向的平均误差从0.0537 m降低到0.0405 m，准确率提高了24.58%.最后，在大范围矩形轨迹上进行了测试，与扩展卡尔曼滤波算法相比，秩卡尔曼滤波在X和Y方向的精度分别提高了23.84%和25.26%，验证了本文提出的算法精度得到了提高。
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.