GhostPilot: Build a GPS-Denied Drone Navigation Stack with Visual SLAM + Agentic AI

The code snippets you've provided outline the core components of a visual-inertial simultaneous localization and mapping (VIO-SLAM) system. Let's break down each part to understand how it contributes to the overall functionality:

1. Frame Class

This class represents individual camera frames in the sequence. Each frame contains information about its pose, timestamp, image data, feature points, and associated landmarks.

• Pose: The position and orientation of the camera at that moment.
• Timestamp: Time when the frame was captured.
• Image Data: Raw pixel values from the camera.
• Feature Points: Detected keypoints in the image with descriptors for matching across frames.
• Landmarks: Known points in 3D space observed by this frame.

2. Feature Matching

This function matches features between consecutive frames to establish correspondences, which are essential for estimating motion and updating landmarks.

3. Pose Estimation

Estimates the relative pose (translation and rotation) between two frames based on matched feature points using a robust method like RANSAC.

4. Triangulation

Determines the 3D position of a landmark from its projections in at least two frames, assuming known camera poses.

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