随着人工智能技术的高速发展，机器人被广泛地应用到工业生产、日常生活、环境探测等多个领域。同步定位与地图创建（SLAM）是机器人实现自主移动的基础，然而在复杂环境中，移动机器人携带的传感器会因外界干扰导致测量精度变差，从而降低了机器人定位建图精度。因此本文对复杂环境下如何提升机器人定位建图精度进行了研究，该研究对加速机器人在实际环境下应用具有重要意义，本文研究内容如下：

（1）对移动机器人SLAM问题进行了描述，介绍了激光SLAM的相关研究方案，详细阐述了基于粒子滤波的SLAM算法原理及框架，并分析了由于粒子滤波算法的局限性导致机器人定位建图精度下降的原因。

（2）针对重采样导致粒子多样性丧失从而降低机器人定位建图精度的问题，提出基于头脑风暴算法优化粒子滤波的SLAM算法（BSO-SLAM）。根据粒子权值大小差异完成K-means聚类操作，并将聚类后的集合进行交叉变异处理，用头脑风暴算法替代粒子滤波的重采样，从而缓解粒子的贫化现象，增加粒子的多样性，使SLAM算法定位精度得到提升。仿真结果表明，相对于GA-FastSLAM2.0算法，BSO-SLAM算法提升了定位建图精度，算法定位精度误差均值降低了24%。

（3）针对BSO-SLAM算法时间复杂度较高的问题，提出基于植物胞群算法的优化SLAM算法（PCSA-SLAM），通过植物胞群算法调整粒子滤波重要性采样后的粒子分布，使粒子集中分布在高似然区域，并省略了重采样过程，从而解决粒子权值退化以及粒子多样性丧失的问题，实现算法滤波精度的提升。仿真结果表明，相对于GFA-FastSLAM2.0算法，PCSA-SLAM算法提高了建图定位精度，算法定位精度误差均值降低了26%，路标预测误差降低了45%。

（4）搭建机器人实验平台，将PCSA-SLAM算法应用到移动机器人定位建图任务中。实验结果表明，在不同环境下，PCSA-SLAM算法可以有效地表示出实际地形的轮廓，以及环境中的障碍物信息，其构建的地图精度更高，鲁棒性更强。

With the rapid development of artificial intelligence technology, robots have been widely used in industrial production, daily life, environmental detection and other fields. Simultaneous localization and mapping (SLAM) is the basis of autonomous mobility of robots. However, in complex environments, sensors carried by mobile robots will degrade the measurement accuracy due to external interference, thus reducing the accuracy of localization and mapping of robots. Therefore, this paper studies how to improve the accuracy of robot positioning and mapping in a complex environment, which is of great significance to accelerate the application of robot in the actual environment. The content of this paper is as follows:

(1) This paper describes the problem of mobile robot SLAM, introduces the related research schemes of laser SLAM, elaborates the principle and framework of SLAM algorithm based on particle filtering, and analyzes the reason why the accuracy of robot localization mapping is decreased due to the limitation of particle filtering algorithm.

(2) In order to reduce the accuracy of robot localization mapping due to the loss of particle diversity caused by resampling, brain storm optimization improved particle filtering SLAM (BSO-SLAM) algorithm was proposed. K-means clustering operation is completed according to the difference of particle weight, and cross-mutation processing is carried out for the set after clustering. The resampling of particle filter is replaced by brain storm optimization, so as to alleviate the dilution of particles, increase the diversity of particles, and improve the localization accuracy of SLAM algorithm. The simulation results show that compared with GA-FastSLAM2.0 algorithm, BSO-SLAM algorithm improves the accuracy of location mapping, and the average positioning accuracy error of the algorithm is reduced by 24%.

(3) For the problem of high time complexity of BSO-SLAM algorithm, the optimized SLAM algorithm based on plant cell swarm algorithm (PCSA-SLAM) is proposed. Plant cell swarm algorithm was used to adjust the particle distribution after the importance sampling of particle filter, so that the particle distribution was concentrated in the high likelihood region, and the resampling process was eliminated. Thus, the degradation of particle weight and the loss of particle diversity can be solved, and the filtering accuracy can be improved. The simulation results show that, compared with the GFA-FastSLAM2.0 algorithm, the PCSA-SLAM algorithm improves the mapping accuracy, the average positioning accuracy error of the algorithm is reduced by 26%, and the road sign prediction error is reduced by 45%.

(4) A robot experimental platform was built, and PCSA-SLAM algorithm was applied to the localization mapping task of mobile robot. The experimental results show that under different environments, the PCSA-SLAM algorithm can effectively represent the contour of the actual terrain and the obstacle information in the environment, and the map constructed by it has higher accuracy and stronger robustness.

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