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.
Abstract (translated)
火星探索需要精确和可靠的地形模型，以确保探测车在不可预测且常有危险的地形中安全导航。立体视觉在探测车感知中扮演关键角色，通过立体匹配生成精确深度图来实现场景重建。目前最先进的火星行星探索方法采用传统的局部块匹配法，在方形窗口内聚合成本，并通过平滑度约束优化视差。然而，这种传统方法经常难以处理低纹理图像、遮挡和重复模式问题，因为它仅考虑有限的相邻像素且缺乏对更广泛场景上下文的理解。 本文提出使用基于超像素细化的半全局匹配（SGM）来减轻固有的块状伪影，并恢复丢失的细节。该方法在保持SGM效率的同时提高了精度，并增加了感知上下文分割以支持更为连贯的深度推断。所提出的这种方法已在三个数据集中进行了评估并取得了成功结果：在火星类比地形中，获得的地形图显示了改进的结构一致性，特别是在斜坡或易受遮挡区域。岩石后面的较大间隙——这是原始视差输出中的常见问题——已大大减少，并且地表细节如小石块和边缘也得到了更准确的捕捉。 另外两个用于测试该方法鲁棒性和适应性的数据集显示了更为精确的视差图和一致地形模型，更适合火星上自主导航的需求，在无遮挡以及全图误差度量方面具有竞争力的精度。 本文概述了整个地形建模过程，从查找对应的特征到生成最终2D导航地图，提供了一个完整的流程，适用于未来行星探索任务中的集成。
URL
https://arxiv.org/abs/2509.05645
