Stereovision Image Processing for Planetary Navigation Maps with Semi-Global Matching and Superpixel Segmentation

2025-09-06

AI Summary

Mars rovers face challenges in terrain modeling due to inaccurate depth estimation caused by low-texture regions, occlusions, and repetitive patterns. To address this, we propose a superpixel-guided semi-global matching (SGM) depth estimation method: an initial disparity map is generated via SGM, followed by a context-aware, edge-preserving post-processing step leveraging superpixel segmentation. This effectively suppresses block artifacts, recovers fine structural details, and enhances depth continuity. The method establishes a complete pipeline from stereo matching to 2D navigability map generation. Experiments on Mars-like terrain and multiple public datasets demonstrate that our approach significantly reduces occlusion-induced holes behind rocks, improves reconstruction accuracy of small stones and object boundaries, and achieves lower disparity error than conventional methods. It exhibits strong robustness and high suitability for planetary surface navigation tasks.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Mars exploration requires precise and reliable terrain models to ensure safe rover navigation across its unpredictable and often hazardous landscapes. Stereoscopic vision serves a critical role in the rover's perception, allowing scene reconstruction by generating precise depth maps through stereo matching. State-of-the-art Martian planetary exploration uses traditional local block-matching, aggregates cost over square windows, and refines disparities via smoothness constraints. However, this method often struggles with low-texture images, occlusion, and repetitive patterns because it considers only limited neighbouring pixels and lacks a wider understanding of scene context. This paper uses Semi-Global Matching (SGM) with superpixel-based refinement to mitigate the inherent block artefacts and recover lost details. The approach balances the efficiency and accuracy of SGM and adds context-aware segmentation to support more coherent depth inference. The proposed method has been evaluated in three datasets with successful results: In a Mars analogue, the terrain maps obtained show improved structural consistency, particularly in sloped or occlusion-prone regions. Large gaps behind rocks, which are common in raw disparity outputs, are reduced, and surface details like small rocks and edges are captured more accurately. Another two datasets, evaluated to test the method's general robustness and adaptability, show more precise disparity maps and more consistent terrain models, better suited for the demands of autonomous navigation on Mars, and competitive accuracy across both non-occluded and full-image error metrics. This paper outlines the entire terrain modelling process, from finding corresponding features to generating the final 2D navigation maps, offering a complete pipeline suitable for integration in future planetary exploration missions.

Problem

• Improving depth map accuracy for Mars rover navigation
• Addressing low-texture and occlusion issues in stereo matching
• Enhancing terrain model consistency in challenging planetary landscapes

Innovation

• Semi-Global Matching with superpixel-based refinement
• Combines SGM efficiency with context-aware segmentation
• Reduces block artifacts and recovers lost details