用深度估计与异常检测结合，提升火星车目标优先级判断准确率
ARTPS: Depth-Enhanced Hybrid Anomaly Detection and Learnable Curiosity Score for Autonomous Rover Target Prioritization
- 融合视觉变换器深度估计与多组件异常检测
- 在火星数据集上达0.94的AUROC和0.87的F1分数
- 适合行星探测机器人自主导航研究者
我们提出ARTPS（自主火星车目标优先级系统），一种结合单目深度估计、异常检测与可学习好奇心评分的混合AI系统，用于行星表面自主探索。该方法利用视觉变换器进行单目深度估计，融合多组件异常检测与加权好奇心评分，综合考虑已知价值、异常信号、深度方差及表面粗糙度。在火星车数据集上，系统达到0.94的AUROC、0.89的AUPRC和0.87的F1分数，性能领先。消融实验表明，该混合融合策略使误报率降低23%，同时保持对多种地形的高检测灵敏度。
原文摘要 · Abstract (English)
We present ARTPS (Autonomous Rover Target Prioritization System), a novel hybrid AI system that combines depth estimation, anomaly detection, and learnable curiosity scoring for autonomous exploration of planetary surfaces. Our approach integrates monocular depth estimation using Vision Transformers with multi-component anomaly detection and a weighted curiosity score that balances known value, anomaly signals, depth variance, and surface roughness. The system achieves state-of-the-art performance with AUROC of 0.94, AUPRC of 0.89, and F1-Score of 0.87 on Mars rover datasets. We demonstrate significant improvements in target prioritization accuracy through ablation studies and provide comprehensive analysis of component contributions. The hybrid fusion approach reduces false positives by 23% while maintaining high detection sensitivity across diverse terrain types.
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