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201 changes: 201 additions & 0 deletions modules/SimpleSLAM/LICENSE
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# Structure From Motion (SfM) - README


https://github.com/user-attachments/assets/f489a554-299e-41ad-a4b4-436e32d8cbd5


## IMPORTANT - LightGlue Installation-
- 1st do this
```
git clone https://github.com/cvg/LightGlue.git && cd LightGlue
python -m pip install -e .
```
- 2nd download data
- https://rpg.ifi.uzh.ch/docs/teaching/2024/kitti05.zip

- put it in folder named 'Dataset'

- 3rd
- intall ```requirements.txt```
- ```bash scripts/run_tracker_visualization.sh```

## INSTALLING COLMAP
![Image showing commands](media/colmap_instruc.png)
use this command - ```ninja -j4 -l 8 > build.log 2>&1```


for pycolmap - https://colmap.github.io/pycolmap/index.html#pycolmap-index

refer the image if when running the command it freezes and crashes
![pycolmap instructions- Image showing commands](media/pycolmap_instruc.png)


## Overview

This repository contains two Python scripts that demonstrate basic Structure-from-Motion (SfM) pipelines:

1. **sfm.py**
- A more classical approach that uses standard OpenCV feature detectors (e.g., SIFT, ORB, AKAZE) and BFMatcher or FLANN to match keypoints between images.
- Performs pose estimation (essential matrix or PnP) and triangulation to build a sparse map of 3D points.
- Uses optional non-linear refinement via scipy’s least squares to improve the estimated camera pose.

2. **sfm_lightglue_aliked.py**
- An enhanced pipeline that integrates neural network-based feature extraction (**ALIKED**) and feature matching (**LightGlue**).
- Demonstrates how modern, learned feature detectors and matchers can improve keypoint reliability and reduce drift.
- Also includes the same core SfM steps (pose estimation, triangulation, optional non-linear refinement).
- Tracks a simple **Absolute Trajectory Error (ATE)** and accumulates a **cumulative translation error** for quick performance checks.

Both scripts are **prototypes** designed primarily for **concept validation and experimentation**. For real-time, production-grade implementations, it’s recommended to integrate a C++ back end (e.g., [Ceres Solver](https://github.com/ceres-solver/ceres-solver)) for optimization and manage heavy-lifting tasks in a more performant environment.

---

## Features

### Common SfM Steps
- **Dataset Loading** (KITTI, Malaga, or custom folder with images).
- **Camera Calibration** for loading intrinsic/extrinsic parameters.
- **Feature Extraction**
- sfm.py: classical (SIFT, ORB, AKAZE)
- sfm_lightglue_aliked.py: ALIKED (learned keypoints + descriptors)
- **Feature Matching**
- sfm.py: BFMatcher or FLANN
- sfm_lightglue_aliked.py: LightGlue (neural network-based matching)
- **Motion Estimation**
- 2D-2D with essential matrix.
- 2D-3D with PnP (once 3D map points are available).
- **Triangulation**
- Convert 2D matches into 3D points.
- **Non-linear Refinement**
- Uses scipy’s Levenberg-Marquardt (`least_squares`) to minimize reprojection error.
- **Basic Stereo Handling** (KITTI, Malaga)
- Combine left and right images for better scale recovery if stereo calibration is present.
- **Trajectory Evaluation**
- **ATE** (Absolute Trajectory Error) if ground truth is available.
- A simple “cumulative translation error” measure.

---

## Requirements

- **Python 3.7+**
- **OpenCV** (>= 4.x recommended)
- **NumPy**
- **Matplotlib** (for visualization)
- **scipy** (for non-linear refinement)
- **tqdm** (for progress bars)
- **PyTorch** (only required for sfm_lightglue_aliked.py, if using LightGlue + ALIKED)
- **lightglue** (the Python package for the LightGlue matching framework)

---

## Usage

### 1. Cloning & Installation
1. Clone this repository:
```bash
git clone https://github.com/your-organization/your-repo.git
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
Or individually:
```bash
pip install opencv-python numpy matplotlib scipy tqdm torch
# plus LightGlue if not already installed
```

### 2. Running **sfm.py**
```bash
python sfm.py --dataset kitti --data_path ./Dataset/kitti
```
- **Arguments**:
- `--dataset`: name of the dataset (kitti, malaga, or custom).
- `--data_path`: path to the dataset folder.
- **Behavior**:
- Loads images, performs feature detection + matching (SIFT, ORB, AKAZE), estimates camera motion, triangulates points.
- Optionally runs non-linear refinement on the pose.
- Plots or logs the results (trajectory, errors).

*(Adjust arguments to match your own script’s CLI if needed.)*

### 3. Running **sfm_lightglue_aliked.py**
```bash
python sfm_lightglue_aliked.py --dataset kitti --data_path ./Dataset/kitti --use_lightglue True
```
- **Arguments**:
- `--dataset`: name of the dataset (kitti, malaga, or custom).
- `--data_path`: path to the dataset folder.
- `--use_lightglue`: enable or disable ALIKED + LightGlue pipeline.
- **Behavior**:
- Loads images, runs ALIKED for feature extraction, and LightGlue for matching (GPU if available).
- Estimates camera motion, triangulates points, performs non-linear refinement if configured.
- Computes:
- **ATE** (Absolute Trajectory Error)
- A “cumulative translation error” measure
- Optionally displays debug visualizations (keypoints, matches, trajectory).

### 4. Visualizations
- **Matplotlib** windows may pop up showing:
- Keypoints and matches for consecutive frames.
- The evolution of the 3D point cloud (if any).
- The camera’s estimated trajectory vs. ground truth (if available).

### 5. Customization
- Modify `THUMPUP_POS_THRESHOLD` and `THUMPUP_ROT_THRESHOLD` for keyframe selection.
- Tweak the **maximum keypoints** or **confidence** in `ALIKED` or **LightGlue** for performance vs. accuracy trade-offs.
- Adjust RANSAC thresholds or non-linear refinement parameters (in `refine()` method) for more robust or faster solutions.

---

## Implementation Details

- **sfm.py**
- Uses OpenCV for feature detection (SIFT, ORB, or AKAZE).
- BFMatcher or FLANN for matching.
- Essential matrix / PnP for pose.
- Minimal keyframe selection logic.

- **sfm_lightglue_aliked.py**
- ALIKED for learned keypoints + descriptors, LightGlue for matching.
- Similar pose estimation logic (PnP, essential matrix).
- Triangulation + refinement steps are nearly the same.
- Typically yields more reliable matches and lower drift.

- **Stereo** logic (KITTI, Malaga) uses left/right cameras for absolute scale.
- **Monocular** is scale-invariant and can produce an arbitrary scale.
- **Error Metrics**:
- **ATE**: Norm of translation difference from ground truth.
- **Cumulative translation error**: Summation of frame-by-frame translation offsets.

---

## Performance & Future Directions

- **Python Prototyping**: Great for algorithmic experimentation but can be slower for large-scale or real-time tasks.
- **Production-Grade**: Offload heavy steps (bundle adjustment, large-scale optimization) to C++.
- **Loop Closure & Full SLAM**: These scripts focus on **Visual Odometry**. Future expansions may include place recognition, pose graph optimization, etc.

---

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