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首页> 外文期刊>International Journal of Advanced Robotic Systems >Novel indoor positioning algorithm based on Lidar/inertial measurement unit integrated system
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Novel indoor positioning algorithm based on Lidar/inertial measurement unit integrated system

机译：基于LIDAR /惯性测量单元集成系统的新型室内定位算法

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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 smallscale 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.
机译：作为移动机器人的重要研究领域，同时本地化和映射技术是实现智能自主移动机器人的核心技术。针对LIDAR的低定位精度（光检测和测距）同时定位和用非线性和非高斯噪声特性映射的问题，本文介绍了一个移动机器人同时定位和映射方法，该定位和映射方法结合了LIDAR和惯性测量单元设置多传感器集成系统并使用等级卡尔曼滤波通过惯性测量单元和LIDAR观测来估计机器人运动轨迹。等级卡尔曼滤波类似于结构的高斯确定性点采样过滤算法，但它不需要满足高斯分布的假设。它完全根据等级统计的相关原理来完全计算采样点和采样点权重。它适用于非线性和非高斯系统。通过对小型弧形轨迹的多个实验测试，我们可以看到与单独的激光雷达同时定位和映射算法相比，新算法将室内移动机器人的平均误差从0.0928米到0.0451米降低，改善精度率为46.39％，y方向的平均误差从0.0772米到0.0405米，从而提高了48.40％的精度率。与扩展卡尔曼滤波融合算法相比，新算法将室内移动机器人的平均误差降低了0.0597米至0.0451米，提高了24.46％的精度率，以及y方向的平均误差0.0537米至0.0405米，提高了24.58％的精度率。最后，我们还在大规模的矩形轨迹上进行了测试，与扩展卡尔曼滤波算法相比，等级卡尔曼滤波分别在x和y方向上提高了23.84％和25.26％的准确度，验证了这方面的准确性本文提出的算法已得到改善。

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