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RatSLAM是模拟鼠类感知环境机制提出的一种定位与构图的导航算法,算法的提出者利用该方法成功地进行了66km的车载试验。在机器人导航领域,RatSLAM是非常优秀的纯视觉仿生导航算法。然而,该算法完全依赖视觉信息,在复杂的环境中存在可靠性低、导航精度不高的问题。在RatSLAM的基础上引入光学双轴速度传感器和MIMU信息,建立了融合光学双轴速度传感器和MIMU信息的航位推算模型,对RatSLAM仿生导航算法进行了合理改进,搭建了硬件系统并进行了动态车载实验。实测结果表明改进的仿生导航算法环境适应性更强、导航精度更高。
RatSLAM is a positioning and compositional navigation algorithm that mimics the mechanism of mouse perception environment. The algorithm proponent used this method successfully to carry on the vehicle test of 66km. In the field of robot navigation, RatSLAM is a very good pure vision biomimetic navigation algorithm. However, the algorithm is totally dependent on visual information and has low reliability and poor navigation precision in complex environment. Based on RatSLAM, an optical biaxial velocity sensor and MIMU information are introduced, a dead reckoning model based on optical biaxial velocity sensor and MIMU information is established, the RatSLAM bionic navigation algorithm is improved reasonably, a hardware system is built and a dynamic Car experiment. The measured results show that the improved biomimetic navigation algorithm is more environment-friendly and has higher navigation accuracy.