An Efficient Algorithm for Learning-Based Visual Localization

TL;DR

This paper introduces a novel, efficient algorithm based on optimal control principles for visual localization in resource-constrained, GPS-denied environments, leveraging Hessian estimation to improve neural network training and convergence.

Contribution

The paper presents a new algorithm that combines optimal control with Hessian estimation to enhance training efficiency and localization accuracy in limited-resource settings.

Findings

Achieves competitive localization accuracy on public datasets.

Demonstrates improved training speed and convergence.

Shows strong generalization capability of the model.

Abstract

This paper addresses the visual localization problem in Global Positioning System (GPS)-denied environments, where computational resources are often limited. To achieve efficient and robust performance under these constraints, we propose a novel algorithm. The algorithm stems from the optimal control principle (OCP). It incorporates diagonal information estimation of the Hessian matrix, which results in training a higher-performance deep neural network and accelerates optimization convergence. Experimental results on public datasets demonstrate that the final model achieves competitive localization accuracy and exhibits remarkable generalization capability. This study provides new insights for developing high-performance offline positioning systems.