# ARTPS: Depth-Enhanced Hybrid Anomaly Detection and Learnable Curiosity Score for Autonomous Rover Target Prioritization

## TL;DR

ARTPS is a hybrid AI system for autonomous planetary exploration that combines depth estimation, anomaly detection, and curiosity scoring to improve target prioritization accuracy on Mars rover datasets.

## Contribution

The paper introduces ARTPS, a novel hybrid AI framework integrating depth estimation, anomaly detection, and learnable curiosity scoring for enhanced autonomous rover target prioritization.

## Key findings

- Achieves state-of-the-art AUROC of 0.94 and F1-Score of 0.87 on Mars datasets.
- Reduces false positives by 23% compared to previous methods.
- Demonstrates significant improvements through ablation studies.

## Abstract

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.