A multi-sensor fusion approach for simultaneous localisation and mapping (SLAM) based on a bio-inspired polarised skylight sensor is presented in this study. The innovation of the proposed approach is that a newly designed bio-inspired polarised skylight sensor, which is inspired by the navigation principle of desert ant, is introduced to improve the accuracy of SLAM. The measurement equations based on a polarised skylight sensor and a lidar are derived to obtain the orientation and position of the mobile robot and landmarks. Three kinds of non-linear filters, extended Kalman filter (EKF), unscented Kalman filter, and particle filter, are adopted and compared to fuse the polarised skylight sensor, lidar, and odometry to estimate the position, orientation, and map in the experiments. Simulation tests and experiments are conducted to validate the effectiveness of the proposed method. The simulations show that the EKF–SLAM with the polarised skylight sensor reduces the error of localisation about 30% and the error of mapping about 25%. Experiments indicate that the proposed EKF–SLAM approach can reduce the error of position and the heading angle, which verifies the proposed EKF–SLAM method, can be used for the outdoor with the low-cost multi-sensor.

中文翻译：

---

具有生物启发性极化天窗传感器的移动机器人的多传感器融合SLAM方法

本研究提出了一种基于生物启发的偏振天窗传感器的同时定位和地图（SLAM）的多传感器融合方法。该方法的创新之处在于引入了一种新设计的，受沙漠蚂蚁导航原理启发的生物启发式偏振天窗传感器，以提高SLAM的精度。导出基于偏振天窗传感器和激光雷达的测量方程，以获得移动机器人和地标的方向和位置。采用了三种非线性滤波器，扩展卡尔曼滤波器（EKF），无味卡尔曼滤波器和粒子滤波器，并将其与极化天光传感器，激光雷达和里程表进行融合，以估计实验中的位置，方向和地图。进行了仿真测试和实验，验证了所提方法的有效性。仿真表明，带偏振天窗传感器的EKF–SLAM可以将定位误差降低约30％，将映射误差降低约25％。实验表明，所提出的EKF–SLAM方法可以减少位置和航向角的误差，这证明了所提出的EKF–SLAM方法可以用于带有低成本多传感器的室外。