Predicting the performance of ORB-SLAM3 on embedded platforms

Abstract

Simultaneous Localization and Mapping (SLAM) is a crucial component to the push towards full autonomy of robotic systems, yet it is computationally expensive and can rarely achieve real-time execution speeds on embedded platforms. Therefore, a need exists to  evaluate the performance of SLAM algorithms in practical embedded environments – this paper addresses this need by creating  prediction models to estimate the performance that ORB-SLAM3 can achieve on embedded platforms. The paper uses three embedded platforms: Nvidia Jetson TX2, Raspberry Pi 3B+ and the Raspberry Pi 4B, to generate a dataset that is used in training and  testing performance prediction models. The process of profiling ORB-SLAM3 aids in the selection of inputs to the prediction model as  well as benchmarking the embedded platforms’ performances by using PassMark. The EuRoC micro aerial vehicle (MAV) dataset is used to generate the average tracking time that the embedded platforms can achieve when executing ORB-SLAM3, which is the target  of the prediction model. The best-performing model has the following results 2.84%, 3.93%, and 0.95 for MAE, RMSE and R2 score  respectively. The results show the feasibility of predicting the performance that SLAM applications can achieve on embedded  platforms.