Navigating the Unknown: Towards biologically-inspired Simultaneous Localization and Mapping

Date of Award


Document Type


Degree Name

Master of Science in Machine Learning


Machine Learning

First Advisor

Dr. Hava Siegelmann

Second Advisor

Dr. Dezhen Song


"In order to successfully perform non-trivial tasks that require spatial navigation, a robot needs to build a map of its environment. Paradoxically however, it needs to build this map while navigating the environment. This problem, known as Simultaneous Localization and Mapping (SLAM), is further complicated by the fact that sensor measurements contain errors which make accurate localization difficult. Animals also need to build maps of their environments to perform tasks required for survival and reproduction. In mammals, the Hippocampal Formation is thought to be the brain area responsible for this task. Various spatially modulated cell types have been discovered in the rodent Hippocampal Formation. While the behavior and function of these cell types have been investigated at depth, how they conspire with each other and with sensory stimuli to create a cognitive map that enables flexible navigation has not. This work introduces a SLAM framework inspired by the workings of the Hippocampal Formation. First, models of Head-Direction cells, Grid Modules and Place cells are integrated to predict the 2D pose from odometry input. Then, visual landmarks observed during odometry are used to calibrate position estimates. The allocentric coordinates of visual cues are encoded in landmark cells and subjected to hebbian plasticity. Grid cells in this model maintained their hexagonal firing fields despite sensor noise. This model also significantly outperformed other existing SLAM algorithms in a simulation environment."


Thesis submitted to the Deanship of Graduate and Postdoctoral Studies

In partial fulfilment of the requirements for the M.Sc degree in Machine Learning

Advisors: Hava Siegelmann, Dezhen Song

with 1 year embargo period

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