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

2025-11-11

AI Summary

Traditional navigation methods face limitations in complex real-world environments: map-free reactive approaches lack robustness; mapping-based methods incur high computational overhead; and learning-based methods suffer from poor generalization and heavy reliance on large-scale training data. Method: This paper proposes a lightweight, map-free real-time visual navigation framework. It introduces Pareto-optimal decision-making into visual navigation for the first time, integrating CNN-based semantic feature extraction, multi-objective optimization (balancing safety and efficiency), and closed-loop visual servo control grounded in the image Jacobian matrix. Contribution/Results: Its core innovation lies in a semantics-driven, unified design for image-space decision-making and control. Experiments demonstrate over 30% improvement in navigation success rate across diverse cluttered environments, while achieving strong generalization, real-time performance (>30 FPS), and low computational cost.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Addresses navigation failures in cluttered environments without maps
• Reduces heavy mapping efforts required by traditional approaches
• Overcomes limited generalization of learning-based navigation solutions

Innovation

• Lightweight image-space framework for navigation
• Combines data-driven semantics and Pareto-optimal decision-making
• Integrates visual servoing for real-time navigation capability