Just added an altitude estimation module to my state estimation library! This C-based implementation now fuses data from multiple sensors (IMU, barometer, and Time of Flight) to provide accurate altitude tracking for drones and robotic systems. #RoboticsEngineering#DroneNavigation#CProgramming
The altitude module uses a 5-state Kalman filter to track altitude, vertical velocity, vertical acceleration, and sensor biases. It handles real-world challenges like sensor dropouts and noise - crucial for actual flight conditions. I've implemented bias estimation for both the barometer and accelerometer to improve long-term stability. 📊 #KalmanFilter#SensorFusion
One of the key features is how it combines different sensor strengths: accelerometer for quick dynamics, barometer for absolute reference, and ToF for precise close-range measurements. The module runs at 100Hz and includes temperature compensation for the barometer - essential for real-world applications. ⚡ #RealTime#Sensors
What sets this implementation apart is its focus on robustness and simplicity. The API is straightforward, making it easy to integrate into existing systems. I've included detailed configuration options for tuning the Kalman filter parameters, process noise, and measurement characteristics. 🛠️ #SoftwareEngineering#Embedded
Testing was a major focus - developed a comprehensive test suite that verifies stability, step response, sensor failure handling, and noise rejection. The tests provide detailed metrics and pass/fail criteria, making it easy to validate performance across different conditions. 🧪 #Testing#QualityAssurance
For those interested in the technical details, the library uses minimal dependencies and is platform-independent. The code is well-documented, and I've included example configurations for common use cases. You can find it alongside the existing attitude estimation module in the C-StateEstimation repository. 💻 #OpenSource#EmbeddedSystems
Future plans include integrating this with the attitude estimation module for full state estimation and adding advanced features like adaptive filtering. Currently working on improving the temperature compensation model and adding more sensor fusion options. 🔄 #Development#Innovation
The module maintains consistent coding standards with the rest of the library and focuses on computational efficiency - crucial for embedded systems. Error handling is robust, with clear status reporting and graceful degradation during sensor failures. 🎯 #CodeQuality#Reliability
Check out the documentation for implementation details and example usage. Happy to receive feedback and contributions from the community! #OpenSource#Robotics#ControlSystems 🤖
ANTSHIV ROBOTICS
🚀 C-StateEstimation: Altitude Module Release
github repo link: github.com/antshiv/stateEstimation
alittude page: github.com/antshiv/stateEstimation/blob/main/src/e…
Just added an altitude estimation module to my state estimation library! This C-based implementation now fuses data from multiple sensors (IMU, barometer, and Time of Flight) to provide accurate altitude tracking for drones and robotic systems. #RoboticsEngineering #DroneNavigation #CProgramming
The altitude module uses a 5-state Kalman filter to track altitude, vertical velocity, vertical acceleration, and sensor biases. It handles real-world challenges like sensor dropouts and noise - crucial for actual flight conditions. I've implemented bias estimation for both the barometer and accelerometer to improve long-term stability. 📊 #KalmanFilter #SensorFusion
One of the key features is how it combines different sensor strengths: accelerometer for quick dynamics, barometer for absolute reference, and ToF for precise close-range measurements. The module runs at 100Hz and includes temperature compensation for the barometer - essential for real-world applications. ⚡ #RealTime #Sensors
What sets this implementation apart is its focus on robustness and simplicity. The API is straightforward, making it easy to integrate into existing systems. I've included detailed configuration options for tuning the Kalman filter parameters, process noise, and measurement characteristics. 🛠️ #SoftwareEngineering #Embedded
Testing was a major focus - developed a comprehensive test suite that verifies stability, step response, sensor failure handling, and noise rejection. The tests provide detailed metrics and pass/fail criteria, making it easy to validate performance across different conditions. 🧪 #Testing #QualityAssurance
For those interested in the technical details, the library uses minimal dependencies and is platform-independent. The code is well-documented, and I've included example configurations for common use cases. You can find it alongside the existing attitude estimation module in the C-StateEstimation repository. 💻 #OpenSource #EmbeddedSystems
Future plans include integrating this with the attitude estimation module for full state estimation and adding advanced features like adaptive filtering. Currently working on improving the temperature compensation model and adding more sensor fusion options. 🔄 #Development #Innovation
The module maintains consistent coding standards with the rest of the library and focuses on computational efficiency - crucial for embedded systems. Error handling is robust, with clear status reporting and graceful degradation during sensor failures. 🎯 #CodeQuality #Reliability
Check out the documentation for implementation details and example usage. Happy to receive feedback and contributions from the community! #OpenSource #Robotics #ControlSystems 🤖
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#CLibrary #StateEstimation #DroneControl #Robotics #EmbeddedSystems #SensorFusion #KalmanFilter #RealTime #Engineering #Programming #IMU #Sensors #Navigation #ControlTheory #AltitudeEstimation
11 months ago | [YT] | 9