Navigating the Wild: Pareto-Optimal Visual Decision-Making in Image Space

TL;DR

This paper introduces a lightweight, real-time visual navigation framework that combines semantic understanding, Pareto-optimal decision-making, and visual servoing to improve navigation in complex environments.

Contribution

It proposes a novel image-space navigation approach that integrates data-driven semantics with Pareto-optimal decision-making, reducing reliance on heavy mapping or large datasets.

Findings

Effective in cluttered environments

Real-time decision-making capability

Reduced mapping and dataset requirements

Abstract

Navigating complex real-world environments requires semantic understanding and adaptive decision-making. Traditional reactive methods without maps often fail in cluttered settings, map-based approaches demand heavy mapping effort, and learning-based solutions rely on large datasets with limited generalization. To address these challenges, we present Pareto-Optimal Visual Navigation, a lightweight image-space framework that combines data-driven semantics, Pareto-optimal decision-making, and visual servoing for real-time navigation.