Wall Detection Via IMU Data Classification In Autonomous Quadcopters
This addresses the challenge of lightweight, sensor-free obstacle detection for autonomous drones, though it is incremental as it applies existing classification methods to a new data source.
The paper tackled the problem of obstacle detection for autonomous drones by using data mining classification techniques on IMU data to predict wall direction based on air-disturbance, achieving 90% accuracy with a RandomForest classifier.
An autonomous drone flying near obstacles needs to be able to detect and avoid the obstacles or it will collide with them. In prior work, drones can detect and avoid walls using data from camera, ultrasonic or laser sensors mounted either on the drone or in the environment. It is not always possible to instrument the environment, and sensors added to the drone consume payload and power - both of which are constrained for drones. This paper studies how data mining classification techniques can be used to predict where an obstacle is in relation to the drone based only on monitoring air-disturbance. We modeled the airflow of the rotors physically to deduce higher level features for classification. Data was collected from the drone's IMU while it was flying with a wall to its direct left, front and right, as well as with no walls present. In total 18 higher level features were produced from the raw data. We used an 80%, 20% train-test scheme with the RandomForest (RF), K-Nearest Neighbor (KNN) and GradientBoosting (GB) classifiers. Our results show that with the RF classifier and with 90% accuracy it can predict which direction a wall is in relation to the drone.