In many simultaneous localization and mapping (SLAM) systems, the map of the environment grows over time as the robot explores the environment. The ever-growing map prevents long-term mapping, especially in large-scale environments. In this paper, we develop a compact cognitive mapping approach inspired by neurobiological experiments. Mimicking the firing activities of neighborhood cells, neighborhood fields determined by movement information, i.e. translation and rotation, are modeled to describe one of the distinct segments of the explored environment. The vertices with low neighborhood field activities are avoided to be added into the cognitive map. The optimization of the cognitive map is formulated as a robust non-linear least squares problem constrained by the transitions between vertices, and is numerically solved efficiently. According to the cognitive decision-making of place familiarity, loop closure edges are clustered depending on time intervals, and then batch global optimization of the cognitive map is performed to satisfy the combined constraint of the whole cluster. After the loop closure process, scene integration is performed, in which revisited vertices are removed subsequently to further reduce the size of the cognitive map. The compact cognitive mapping approach is tested on a monocular visual SLAM system in a naturalistic maze for a biomimetic animated robot. Our results demonstrate that the proposed method largely restricts the growth of the size of the cognitive map over time, and meanwhile, the compact cognitive map correctly represents the overall layout of the environment. The compact cognitive mapping method is well suitable for the representation of large-scale environments to achieve long-term robot navigation.

在许多同时定位和映射 (SLAM) 系统中，随着机器人探索环境，环境地图会随着时间的推移而增长。不断增长的地图阻止了长期映射，尤其是在大规模环境中。在本文中，我们开发了一种受神经生物学实验启发的紧凑型认知映射方法。模拟邻域细胞的发射活动，对由运动信息（即平移和旋转）确定的邻域场进行建模，以描述所探索环境的不同部分之一。避免将具有低邻域场活动的顶点添加到认知图中。认知图的优化被表述为一个鲁棒的非线性最小二乘问题，该问题受顶点之间的转换约束，并且可以有效地进行数值求解。根据地点熟悉度的认知决策，根据时间间隔对闭环边进行聚类，然后对认知图进行批量全局优化以满足整个聚类的组合约束。在闭环过程之后，执行场景集成，其中重新访问的顶点随后被移除以进一步减小认知图的大小。紧凑认知映射方法在仿生动画机器人的自然迷宫中的单目视觉 SLAM 系统上进行了测试。我们的结果表明，所提出的方法在很大程度上限制了认知图的大小随时间的增长，同时，紧凑的认知图正确地代表了环境的整体布局。