Custom hardware architectures for embedded high-performance and low-power SLAM

Abstract

Simultaneous localisation and mapping (SLAM) is central to many emerging applications such as autonomous robotics and augmented reality. These require an accurate and information rich reconstruction of the environment which is not provided by the current state-of-the-art in embedded SLAM which focuses on sparse, feature-based methods. SLAM needs to be performed at real-time, with a low latency. At the same time, dense SLAM that can provide a high level of reconstruction quality and completeness comes with high computational and power requirements, while platforms in the embedded space often come with significant power and weight constraints.

Towards overcoming this challenge, this thesis presents FPGA-based custom hardware architectures that offer significantly higher performance than general purpose embedded hardware for SLAM, but with the same low-power requirements. The works begins by discussing the characteristics and computational patterns of this type of application, focusing on a state-of-the-art semi-dense direct SLAM algorithm. Then custom hardware architectures are presented and evaluated as they emerged from this research work. These combine many novel features to achieve a performance on-par with optimised software on a high-end multicore desktop CPU but with more than an order-of-magnitude better performance-per-watt.

The two high-performance, power-efficient architectures for the two interdependent tasks that comprise the core of real-time SLAM, are designed to work alongside a mobile CPU running a full operating system, and scale in terms of resources to provide a solution that can be adapted to most off-the-shelf FPGA-SoCs. Thus, as well as offering the necessary performance and performance-per-watt to enable advanced semi-dense SLAM on mobile power-constrained platforms, they stand to bridge the gap between custom hardware and research in algorithms and robotic vision as they can be adapted and re-used more easily than traditional custom hardware architectures.