Navigating the Landscape for Real-time Localisation and Mapping for Robotics and Virtual and Augmented Reality
This work addresses the problem of enabling efficient SLAM implementation for robotics and AR/VR applications, though it is incremental as it focuses on tools and methodologies rather than new algorithms.
This paper tackles the computational challenge of real-time 3D visual understanding (SLAM) by developing tools and methodologies to help specialists select and configure algorithms and hardware to meet performance, accuracy, and energy goals. The results include automated design space exploration, simulation tools for optimizing architectures, and adaptive runtime solutions for accelerated SLAM.
Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge. Often referred to as SLAM (Simultaneous Localisation and Mapping), it is central to applications spanning domestic and industrial robotics, autonomous vehicles, virtual and augmented reality. This paper describes the results of a major research effort to assemble the algorithms, architectures, tools, and systems software needed to enable delivery of SLAM, by supporting applications specialists in selecting and configuring the appropriate algorithm and the appropriate hardware, and compilation pathway, to meet their performance, accuracy, and energy consumption goals. The major contributions we present are (1) tools and methodology for systematic quantitative evaluation of SLAM algorithms, (2) automated, machine-learning-guided exploration of the algorithmic and implementation design space with respect to multiple objectives, (3) end-to-end simulation tools to enable optimisation of heterogeneous, accelerated architectures for the specific algorithmic requirements of the various SLAM algorithmic approaches, and (4) tools for delivering, where appropriate, accelerated, adaptive SLAM solutions in a managed, JIT-compiled, adaptive runtime context.