A Brain-Inspired Compact Cognitive Mapping System
This work addresses the challenge of long-term mapping for robots in large-scale environments, though it appears incremental as it builds on existing SLAM methods with a focus on compactness.
The paper tackles the problem of ever-growing maps in SLAM systems for long-term robot mapping by developing a brain-inspired compact cognitive mapping approach that restricts map size growth while correctly representing the environment, as tested in a rat-like maze with a monocular visual SLAM system.
As the robot explores the environment, the map grows over time in the simultaneous localization and mapping (SLAM) system, especially for the large scale environment. The ever-growing map prevents long-term mapping. In this paper, we developed a compact cognitive mapping approach inspired by neurobiological experiments. Inspired from neighborhood cells, neighborhood fields determined by movement information, i.e. translation and rotation, are proposed to describe one of distinct segments of the explored environment. The vertices and edges with movement information below the threshold of the neighborhood fields are avoided adding to the cognitive map. The optimization of the cognitive map is formulated as a robust non-linear least squares problem, which can be efficiently solved by the fast open linear solvers as a general problem. According to the cognitive decision-making of familiar environments, loop closure edges are clustered depending on time intervals, and then parallel computing is applied to perform batch global optimization of the cognitive map for ensuring the efficiency of computation and real-time performance. 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. A monocular visual SLAM system is developed to test our approach in a rat-like maze environment. Our results suggest that the method largely restricts the growth of the size of the cognitive map over time, and meanwhile, the compact cognitive map correctly represents the overall layer of the environment as the standard one. Experiments demonstrate that our method is very suited for compact cognitive mapping to support long-term robot mapping. Our approach is simple, but pragmatic and efficient for achieving the compact cognitive map.