摘要
针对传统的概率假设密度SLAM(PHD-SLAM)算法因粒子退化和耗尽导致估计精度低的问题,提出一种基于蚂蚁聚类的概率假设密度SLAM(ant-PHD-SLAM)算法。其主要的特点:用ant-PHD滤波器实现对地图特征状态和数目的估计,用移动蚂蚁估计器完成对机器人位姿的后验估计。与传统SLAM算法相比,新提出的SLAM算法用人工蚂蚁代替简单的粒子去实现对目标的估计,得益于蚂蚁强大的搜索机制,新算法的估计精度得到明显提高。通过仿真实验,将所提出的算法与传统PHD-SLAM算法进行比较,结果表明所提出算法有效提高了机器人定位估计精度和构图精度。
For the traditional PHD-SLAM algorithm has low estimation accuracy due to particle degradation and depletion,a novel SLAM algorithm based on ant clustering and probabilistic hypothesis density is proposed in this paper.The main contributions is using an ant-PHD filter to jointly estimate the locations and number of features,moreover,a moving ant estimator is developed for estimating maneuvering vehicle trajectory.In contrast to the traditional SLAM algorithm,the novel ant-PHD-SLAM algorithm uses artificial ants instead of simple particles to search the specific target.Benefit from ant powerful search mechanism,the estimation accuracy of the new algorithm has been significantly improved.
出处
《工业控制计算机》
2018年第3期67-69,共3页
Industrial Control Computer
关键词
移动机器人
SLAM
概率假设密度滤波
蚁群优化算法
mobile robot
simultaneous localization and mapping
probability hypothesis density filter
ant colony optimization
