Graph-Based Simultaneous Localization and Mapping: Computational Complexity Reduction on a Multicore Heterogeneous Architecture

This article deals with the computational complexity issue of graphbased simultaneous localization and mapping (SLAM). SLAM allows a robot that is navigating in an unknown environment to build a map of this environment while simultaneously determining the robot pose on this map. Graph-based SLAM is a smoothing method that uses a graph to represent and solve the SLAM problem. We first propose a graph construction that takes advantage of the incremental and sparse characteristics of graph-based SLAM. This incremental construction is exploited to perform several algorithmic optimizations. Second, we present a study of using a heterogeneous architecture to implement the graph-based SLAM algorithm. Indeed, the emergence of recent heterogeneous embedded architectures should lead to a great advance in the design of embedded systems-based robotics applications. As a result of this study, an algorithm-architecture mapping is proposed for a central processing unit-graphics processing unit (CPU-GPU)-based architecture. The study also investigates how this kind of architecture can speed up graph-based SLAM by offloading some critical compute-intensive tasks of the algorithm on the GPU. Some common data sets are used to compare our implementations to the state of the art.