Enabling Cognitive Autonomy on Small Drones by Efficient On-Board Embedded Computing: An ORB-SLAM2 Case Study

Abstract

In this paper, we present a case study which investigates whether/how Simultaneous Localization and Mapping (SLAM), e.g., the ORB-SLAM2 application, can be executed on a small, energy-efficient, multi-processor embedded platform with an ARM big.LITTLE architecture, e.g., the ODROID-XU4 platform, mounted on a small drone with a limited energy budget while meeting real-time performance requirements. More specifically, we model and implement ORB-SLAM2 as a Kahn Process Network (KPN) which exploits pipeline parallelism and enables efficient mapping and execution of ORB-SLAM2 onto ODROID-XU4. Moreover, our KPN model enables the application of generic model transformations to exploit data-level parallelism as well. Then, we propose and implement, on top of the Linux operating system, an environment for efficient execution of applications modeled as KPNs. Finally, we perform a simple design space exploration (DSE) to investigate the trade-off between system performance and power consumption when alternative ORB-SLAM2 KPNs are executed on different configurations of the ODROID-XU4 platform. The obtained results of this DSE clearly show the feasibility of running ORB-SLAM2 on ODROID-XU4 in real time with a limited power budget for a given range of flying time, thereby enabling cognitive autonomy on small drones.