A Simple SLAM Solution.

With both PROS and VEXCode support

Getting Started

To start off with WhoopLib, head to: https://CorniiDog.github.io/WhoopLib/

Links

WhoopLib Documentation

WhoopLibVEXCode Github

WhoopLibPROS Github

WhoopLibPython Github

Features

Odometry & Pose Estimation

• Visual odometry / pose estimation
• Wheel odometry / pose estimation (inspired by JAR-Template)
• Fusion odometry (visual + wheel) with rolling average filter

Control & Motion

• WhoopController with auto-configuration for Split Arcade, Tank, Left Stick Arcade, and Right Stick Arcade
• Path generation: Dubins curves (thanks to Andrew Walker) and Pure Pursuit
• Point-to-point navigation (move between Point A and Point B)
• High-level motion primitives: precise turning (by degrees / to face coordinates), forward/reverse movement with historical position memory for resilience
• General PID controller with anti-windup (kR, aka "retracted windup")
• Slew rate limiter for motor movements
• Motor voltage-to-speed linearization

Utilities & Abstractions

• TwoDPose class simplifying linear algebra, modeled with similarities to Roblox’s CFrames
• Units system (_in, _v, _mm, etc.)
• Simplified MicroSD card file system
• Autonomous selector with optional persistent saving via MicroSD

Documentation & Ecosystem

• Comprehensive and continuously updated documentation for a low floor and high ceiling

Roadmap (Needs Maintainer)

• Object Detection and Gridded Permanence system
• Detecting other robots that impede the path of the robot, and drive around
• Implementation of a Jetson Orion Nano instead of the End-Of-Line (EOL) Jetson Nano
• Implementation of a better SLAM solution instead of relying on the EOL Realsense T265 Camera
• Capability to Use Different Devices like Oak-D, etc. for Visual Odometry
• Capability to Use Lidar
• Virtual Highway system

Important

WhoopLib is an independent, student-led project and is not endorsed by or affiliated with Texas A&M University.

Important

The Python/Jetson backend is experimental. There is no automatic ML data transfer to V5; users must implement their own integration. Odometry fusion depends on the factory calibration quality of the T265, which can vary over time and between units.

Acknowledgements

• E-Bots πLons: Odometry Documentation
• JAR-Template: Odometry Inspiration
• Librealsense: Depth Capturing
• Andrew Walker: Path Generation with Dubins-Curves
• LearnCpp: Resource for learning C++

License

Distributed under the MIT License.