NeuroLoc: Encoding Navigation Cells for 6-DOF Camera Localization

TL;DR

NeuroLoc introduces a biologically inspired camera localization method that enhances robustness and accuracy in complex environments by mimicking brain navigation cells and incorporating historical information, orientation, and 3D grid predictions.

Contribution

The paper presents a novel neurobiological approach for camera localization, integrating Hebbian learning, head direction cells, and 3D grid prediction to improve robustness and accuracy.

Findings

Improves pose regression performance in complex environments.

Enhances robustness with single-image input.

Outperforms existing methods on benchmark datasets.

Abstract

Recently, camera localization has been widely adopted in autonomous robotic navigation due to its efficiency and convenience. However, autonomous navigation in unknown environments often suffers from scene ambiguity, environmental disturbances, and dynamic object transformation in camera localization. To address this problem, inspired by the biological brain navigation mechanism (such as grid cells, place cells, and head direction cells), we propose a novel neurobiological camera location method, namely NeuroLoc. Firstly, we designed a Hebbian learning module driven by place cells to save and replay historical information, aiming to restore the details of historical representations and solve the issue of scene fuzziness. Secondly, we utilized the head direction cell-inspired internal direction learning as multi-head attention embedding to help restore the true orientation in similar scenes. Finally, we added a 3D grid center prediction in the pose regression module to reduce the final wrong prediction. We evaluate the proposed NeuroLoc on commonly used benchmark indoor and outdoor datasets. The experimental results show that our NeuroLoc can enhance the robustness in complex environments and improve the performance of pose regression by using only a single image.