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

TL;DR

The paper addresses the challenge of robust navigation in cluttered real-world environments without relying on explicit maps or heavy training data. It proposes Pareto-Optimal Visual Navigation (POVNav), a two-module framework that uses semantic segmentation to build a navigability image and an image-space planner that selects a Horizon Optic Goal (HOG) from the visual horizon via Pareto-based scalarization, followed by visual servoing. The authors provide theoretical guarantees (weak Pareto optimality and local stability) and analyze computational complexity, complemented by extensive simulations and real-world experiments across indoor/outdoor, static/dynamic, and adverse conditions. The work demonstrates improved success rates and shorter paths than baselines, with robust performance under segmentation noise and domain shifts, and includes practical details for real-time deployment and a release of code. The approach offers a scalable, modular alternative to fully map-based or purely end-to-end methods, enabling reliable, low-compute navigation in varied environments with potentially dynamic obstacles.

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.

Figures (23)

• Figure 1: A motivating example of visual semantic navigation in the wild. Visual semantic navigation enables the robot to interpret the semantic meaning of environmental elements and adapt its path to diverse terrain conditions. The green path represents the shortest route, passing through mud and water, while the yellow path avoids these non-traversable areas. On the right panels, from top to bottom, appear the observed image, segmented image, and a planning image illustrating a semantically-aware, safe path.
• Figure 2: System Diagram of the Proposed Framework. The system diagram illustrates the framework's operational flow. The robot's observation consists of an RGB image, which is processed by the perception module to generate a segmented image using semantic segmentation. Based on the robot's traversability capabilities, navigable and non-navigable classes are defined. Utilizing this definition of navigability, a visual horizon is created to produce a navigability image. The robot then plans a visual path on this navigability image and employs visual servoing to navigate through the environment.
• Figure 3: The planning image and its corresponding input image are shown. The reference frame is fixed at the bottom center whose $x$-axis is shown by red arrow and $y$-axis is shown by green arrow.
• Figure 4: Illustration of the goal angle mapping on the image border. (a) Visualizes the mapping of goals located in front of the robot, corresponding to angles $[-\pi/2, \pi/2]$, onto the left, top, and right borders of the image. Blue circles indicate sample POG points for the goal directions at $-\pi/2$, $0$, and $\pi/2$, with the green circle denoting the robot's current position. The two arrows illustrate example mappings for representative goal directions located to the left and right of the robot’s heading. (b) Illustrates how goals located behind the robot, with angles in $(\pi/2, \pi]$ and $(-\pi/2, -\pi)$, are projected onto an extended virtual border and then re-mapped to the bottom edge using a dashed projection. Black circles represent virtual goal locations, while blue circles denote actual goal projections.
• Figure 5: An illustration of the raw and processed navigability image.

Theorems & Definitions (16)

• Definition 1: Non-Navigable Areas
• Definition 2: Navigability Image
• Definition 3: Visual Horizon
• Definition 4: Traversability Objective
• Definition 5: Navigation Objective
• Definition 6: Exploration Objective
• Definition 7: Pareto Optimal Solutions
• Lemma 1: Optimal Subgoal on Visual Horizon
• Definition 8: Proximity Feature
• Definition 9: Alignment Feature