Improved Accuracy of Robot Localization Using 3-D LiDAR in a Hippocampus-Inspired Model

TL;DR

This paper introduces a 3D boundary vector cell model inspired by hippocampal neurons, enhancing robot localization accuracy in complex environments by incorporating vertical angular sensitivity into boundary detection.

Contribution

It extends existing 2D BVC models to 3D, enabling robots to better disambiguate locations in complex, vertically varied environments, improving spatial localization.

Findings

3D BVC model improves localization in complex environments

Model matches 2D performance in simple, flat environments

Significantly reduces spatial ambiguities and aliasing in 3D spaces

Abstract

Boundary Vector Cells (BVCs) are a class of neurons in the brains of vertebrates that encode environmental boundaries at specific distances and allocentric directions, playing a central role in forming place fields in the hippocampus. Most computational BVC models are restricted to two-dimensional (2D) environments, making them prone to spatial ambiguities in the presence of horizontal symmetries in the environment. To address this limitation, we incorporate vertical angular sensitivity into the BVC framework, thereby enabling robust boundary detection in three dimensions, and leading to significantly more accurate spatial localization in a biologically-inspired robot model. The proposed model processes LiDAR data to capture vertical contours, thereby disambiguating locations that would be indistinguishable under a purely 2D representation. Experimental results show that in environments with minimal vertical variation, the proposed 3D model matches the performance of a 2D baseline; yet, as 3D complexity increases, it yields substantially more distinct place fields and markedly reduces spatial aliasing. These findings show that adding a vertical dimension to BVC-based localization can significantly enhance navigation and mapping in real-world 3D spaces while retaining performance parity in simpler, near-planar scenarios.