MmWave Radar-based Deep Learning Collision Prediction

Abstract: Autonomous drone navigation in classical approaches typically involves constructing a map representation and employing path planning and collision checking algorithms within that map. Recently, novel deep learning techniques combined with depth camera observations have emerged as alternative approaches capable of achieving comparable collision-free performance. While these methods have demonstrated effective collision-free performance in dense environments, they rely on low-noise range or visual data, which may not be feasible in extreme degraded environments characterized by factors such as dust, smoke, weak geometries, or low-texture areas. A possible alternative is to leverage recent progress in mmWave radar imaging, which previously has produced data of insufficient resolution for such purposes. Through the use of a Variational Autoencoder and existing collision prediction algorithms, the goal of this study is to prove the use of mmWave radar for navigating difficult environments. The results of the study exhibit successful navigation in simulated scenarios featuring sparse obstacles. Additionally, results of utilizing real-world mmWave radar data in example scenarios is provided to demonstrate the potential for further application of this technology.