用边缘渲染与加权汉明相似度，实现低算力下高鲁棒性视觉定位
Robust Visual Localization in Compute-Constrained Environments by Salient Edge Rendering and Weighted Hamming Similarity
- 通过自定义渲染生成边缘特征，结合专为边缘设计的匹配度量
- 在低精度、无纹理3D模型下仍保持高定位准确率，优于现有方法
- 适合部署在资源受限的通用硬件，如火星探测机器人
针对概念性的火星样本返回任务中，机械臂需在严重硬件限制下对多个目标进行低间隙抓取与插入的6自由度物体位姿估计问题，本文提出一种新型定位算法。该方法结合自定义渲染器与专为边缘域设计的新模板匹配度量，仅使用低保真、无纹理的3D模型作为输入，实现鲁棒的位姿估计。在合成数据集、地球物理试验平台以及火星原位影像上的大量评估表明，本方法在计算与内存受限条件下，无论鲁棒性还是准确性均持续超越现有最先进方法，从而为通用硬件上低成本、高可靠的定位提供了新可能。
原文摘要 · Abstract (English)
We consider the problem of vision-based 6-DoF object pose estimation in the context of the notional Mars Sample Return campaign, in which a robotic arm would need to localize multiple objects of interest for low-clearance pickup and insertion, under severely constrained hardware. We propose a novel localization algorithm leveraging a custom renderer together with a new template matching metric tailored to the edge domain to achieve robust pose estimation using only low-fidelity, textureless 3D models as inputs. Extensive evaluations on synthetic datasets as well as from physical testbeds on Earth and in situ Mars imagery shows that our method consistently beats the state of the art in compute and memory-constrained localization, both in terms of robustness and accuracy, in turn enabling new possibilities for cheap and reliable localization on general-purpose hardware.
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