Ultra low-power visual odometry for nano-scale unmanned aerial vehicles

One of the fundamental functionalities for autonomous navigation of Unmanned Aerial Vehicles (UAVs) is the hovering capability. State-of-the-art techniques for implementing hovering on standard-size UAVs process camera stream to determine position and orientation (visual odometry). Similar techniques are considered unaffordable in the context of nano-scale UAVs (i.e. few centimeters of diameter), where the ultra-constrained power-envelopes of tiny rotor-crafts limit the onboard computational capabilities to those of low-power microcontrollers. In this work we study how the emerging ultra-low-power parallel computing paradigm could enable the execution of complex hovering algorithmic flows onto nano-scale UAVs. We provide insight on the software pipeline, the parallelization opportunities and the impact of several algorithmic enhancements. Results demonstrate that the proposed software flow and architecture can deliver unprecedented GOPS/W, achieving 117 frame-per-second within a power envelope of 10 mW