🗺️ Visual Odometry (VO) — One-Month Project Plan

A structured 4-week plan to implement a feature-based VO system from scratch. Aligned with classical VO architecture:

• Image Sequence
• Feature Detection
• Feature Matching
• Motion Estimation
• Local Optimization

📆 Week-by-Week Plan

✅ Week 1: Setup & Feature-Based VO Prototype

🎯 Goal: Get a working pipeline from image input to trajectory output.

• Set up development environment (Python + OpenCV + NumPy).
• Choose and load dataset (e.g., KITTI, EUROC, or your own camera).
• Load monocular or stereo frames.

Implement:

• Feature extraction (e.g., ORB or SIFT).
• Feature matching (e.g., FLANN or brute-force matcher).
• Estimate motion:
  • Essential matrix with Five-Point RANSAC.
  • Recover pose (rotation + translation).
• Triangulate 3D points from matched features.
• Visualize 2D/3D trajectory (Matplotlib / Open3D).

✅ Deliverable: Basic VO pipeline with visual trajectory on dataset.

✅ Week 2: Improve Robustness & Accuracy

🎯 Goal: Make the pipeline more robust and less sensitive to noise.

• Replace brute-force matching with:
  • KLT tracker (e.g., cv2.calcOpticalFlowPyrLK), or
  • Efficient matcher with ratio test.
• Add a motion model or frame-to-frame filtering.
• Tune RANSAC threshold to reduce outliers.
• Add scale recovery (using stereo baseline or known motion if monocular).

✅ Deliverable: Smoother and more consistent VO output across sequences.

✅ Week 3: Add Optimization & Visual Backend

🎯 Goal: Optimize motion estimation over multiple frames.

• Add a pose graph or keyframe-based structure.
• Integrate Bundle Adjustment:
  • Use libraries like g2o or Ceres Solver.
• Optimize poses and 3D landmarks together.
• Keyframe selection/culling: keep one every N frames or when motion exceeds threshold.

✅ Deliverable: Refined, consistent map and camera trajectory.

✅ Week 4: Evaluation + Real-Time Extension

🎯 Goal: Evaluate system, improve performance, and test real-time capability.

• Compare estimated trajectory with ground truth using:

  • ATE (Absolute Trajectory Error)
  • RPE (Relative Pose Error)

• Plot error metrics and visualize drift.

• Optional:

  • Integrate IMU for better scale estimation.
  • Run real-time VO using a USB/RPi camera.
  • Add a basic ROS2 wrapper around the pipeline.

• Finalize code:

  • Write README, add example scripts.
  • Record and share video demos.

✅ Deliverable: Shareable, reproducible VO system with error plots and visual output.

🔧 Tools & Libraries Used

• Python 3, OpenCV
• NumPy, Matplotlib, Open3D
• g2o / Ceres (for optimization)
• Optional: ROS2, IMU tools

💡 Tip: You can extend this pipeline toward SLAM by adding loop closure detection and global optimization later.

Charts

Hugo Blox supports the popular Plotly format for interactive data visualizations. With Plotly, you can design almost any kind of visualization you can imagine!

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the Plotly JSON Editor useful.

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example flowchart:

```mermaid
graph TD
A[Hard] -->|Text| B(Round)
B --> C{Decision}
C -->|One| D[Result 1]
C -->|Two| E[Result 2]
```

renders as

An example sequence diagram:

```mermaid
sequenceDiagram
Alice->>John: Hello John, how are you?
loop Healthcheck
    John->>John: Fight against hypochondria
end
Note right of John: Rational thoughts!
John-->>Alice: Great!
John->>Bob: How about you?
Bob-->>John: Jolly good!
```

renders as

An example class diagram:

```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```

renders as

An example state diagram:

```mermaid
stateDiagram
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```

renders as

Data Frames

Save your spreadsheet as a CSV file in your page’s folder and then render it by adding the Table shortcode to your page:

{{< table path="results.csv" header="true" caption="Table 1: My results" >}}

renders as

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