基于禁忌搜索的混沌蚁群算法在SLAM数据关联中的应用

Chaos Ant Colony Algorithm Based on Tabu Search for Data Association of SLAM

  • 摘要: 为解决SLAM的数据关联问题,提出了基于禁忌搜索的混沌蚁群算法,利用蚁群算法的正反馈和并行搜索能力构建初始解并进行优化. 在全局信息素更新时加入混沌扰动,以跳出局部极值,利用禁忌搜索算法的特性,扩大解的搜索空间,得到全局最优解. 在无人机SLAM仿真环境下进行试验,仿真结果表明该方法极大地提高了数据关联率,该算法是有效可行的.

     

    Abstract: To solve the data association problem of simultaneous localization and mapping (SLAM), a chaos ant colony algorithm based on tabu search is proposed in this work. Firstly, the initial solution was built and optimized by use of the characters of positive feedback and parallel search of ant colony algorithm, and chaos disturbance was added to pheromone update to dap out local optimal. Then the search space was expanded to obtain global optimal by the character of tabu search. Finally the proposed algorithm was tested in UAV SLAM environment. The results demonstrate that the proposed algorithm could boost the rate of data association greatly. And it is effective and feasible.

     

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