Wall Detection Via IMU Data Classification In Autonomous Quadcopters

TL;DR

This paper demonstrates that IMU data combined with data mining classification techniques can accurately detect the presence and direction of walls relative to a drone, reducing reliance on external sensors.

Contribution

It introduces a novel approach using airflow modeling and IMU data to classify obstacle location, avoiding additional sensors and environmental instrumentation.

Findings

RandomForest classifier achieves 90% accuracy in wall direction detection.

Higher level airflow features improve obstacle classification.

IMU-based detection reduces payload and power consumption.

Abstract

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.