Project Overview

Developed an advanced instance segmentation model for lunar crater detection to support NASA's autonomous moon navigation missions. The project aimed to create a robust, efficient system capable of identifying and mapping craters in real-time, enabling precise spacecraft navigation on the lunar surface.

Key Achievements

Navigation Accuracy

Reduced navigation error to 3.08 km, representing an 80% improvement over the previous best model's 15 km error rate. This dramatic improvement in accuracy is critical for safe landing site selection and autonomous rover navigation on the Moon's surface.

Performance Optimization

Achieved a 4-second inference time, well within the 20-second mission-critical constraint. This 5x safety margin ensures reliable real-time processing even under suboptimal conditions, crucial for time-sensitive navigation decisions during descent and landing operations.

Memory Efficiency

Optimized the model to use only 1.4 GB peak memory, a 65% reduction compared to baseline models. This optimization was essential for deployment on space-constrained hardware systems with limited computational resources, typical of spacecraft electronics.

Detection Accuracy

Achieved a 50% accuracy improvement over baseline models in crater detection and segmentation. This enhancement significantly improves the reliability of navigation systems that depend on accurate crater identification for position estimation.

Automated Data Pipeline

Built a sophisticated automated labeling pipeline that processed 100,000+ craters across 3,700 images, eliminating approximately 1,000 hours of manual annotation work. This pipeline not only accelerated model training but also ensured consistent, high-quality labels across the entire dataset.

Technical Implementation

The project leveraged state-of-the-art deep learning techniques using PyTorch, implementing custom architectures optimized for instance segmentation tasks. Key technical components included:

• Custom neural network architecture designed for lunar terrain features
• Advanced data augmentation techniques to handle varying lighting conditions and surface textures
• Model compression and quantization techniques to meet memory constraints
• Optimized inference pipeline for real-time processing capabilities
• Robust validation framework to ensure reliability across diverse lunar landscapes

Impact

This crater detection system represents a significant advancement in autonomous lunar navigation technology. The combination of high accuracy, low latency, and efficient resource utilization makes it suitable for deployment in actual space missions, contributing to NASA's goals of establishing sustainable lunar presence and supporting future manned missions to the Moon.