Exploiting Data Parallelism in Graph-Based Simultaneous Localization and Mapping: A Case Study with GPU Accelerations

Abstract

Graph-based simultaneous localization and mapping (G-SLAM) is an intuitive SLAM implementation where graphs are used to represent poses, landmarks and sensor measurements when a mobile robot builds a map of the environment and locates itself in it. Being a very important application employed in many realistic scenarios, estimating the whole environment and all trajectories through solving graph problems for SLAM can incur a large amount of computation and consume a significant amount of energy. For the purpose of improving both performance and energy efficiency, we have unveiled the critical path of the G-SLAM algorithm in this paper and implemented a GPU-based solution to aid it. Furthermore, we have attempted to offload performance-critical components (such as matrix inversions when updating the trajectory) in the G-SLAM process into GPUs through CUDA to exploit data parallelism. With our solution, we observe a speed-up of up to 19.7x and an energy saving of up to 83.7% over a modern workstation class x86 CPU; while on a platform dedicated for edge computing (NVIDIA Jetson Nano), we achieve a speed-up of up to 2.5x and an energy saving of up to 6.4% with its integrated GPU, respectively.