A Brain-Inspired Compact Cognitive Mapping System

10/09/2019
by   Taiping Zeng, et al.
0

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.

READ FULL TEXT

page 1

page 6

page 7

research
09/09/2018

Simultaneous Localization and Mapping (SLAM) using RTAB-MAP

This paper implements Simultaneous Localization and Mapping (SLAM) techn...
research
04/27/2022

The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection

Where am I? This is one of the most critical questions that any intellig...
research
09/19/2022

MeSLAM: Memory Efficient SLAM based on Neural Fields

Existing Simultaneous Localization and Mapping (SLAM) approaches are lim...
research
07/15/2021

A life-long SLAM approach using adaptable local maps based on rasterized LIDAR images

Most real-time autonomous robot applications require a robot to traverse...
research
03/06/2020

StereoNeuroBayesSLAM: A Neurobiologically Inspired Stereo Visual SLAM System Based on Direct Sparse Method

We propose a neurobiologically inspired visual simultaneous localization...
research
10/04/2021

Geometry-based Graph Pruning for Lifelong SLAM

Lifelong SLAM considers long-term operation of a robot where already map...
research
05/20/2020

Maplets: An Efficient Approach for Cooperative SLAM Map Building Under Communication and Computation Constraints

This article introduces an approach to facilitate cooperative exploratio...

Please sign up or login with your details

Forgot password? Click here to reset