edgeSLAM2: Rethinking Edge-Assisted Visual SLAM with On-Chip Intelligence

Edge-assisted visual SLAM stands as a pivotal enabler for emerging mobile applications, such as search-and-rescue, smart logistics, and industrial inspection. Limited by the computing capability of lightweight mobile devices like MAVs, current innovations balance system accuracy and efficiency by allocating lightweight and time-sensitive tracking tasks to mobile devices, while offloading the more resource-intensive yet delay-tolerant map optimization tasks to the edge. However, our pilot study in a large-scale oil field reveals several limitations of such a tracking-optimization decoupled paradigm, arising due to the disruption of inter-dependencies between the two tasks concerning data, resources, and threads.In this paper, we design and implement edgeSLAM2, an innovative system that reshapes the edge-assisted visual SLAM paradigm by tightly integrating tracking and partial-yet-crucial optimization on mobile. edgeSLAM2 harnesses the hierarchical and heterogeneous computing units offered by the latest commercial systems-on-chip (SoCs) to enhance the computational capacity of mobile devices, which in turn, allows edgeSLAM2 to design a suit of novel algorithms for map sync, optimization, and tracking that accommodate such architectural upgrade. By fully embracing the on-chip intelligence, edgeSLAM2 simultaneously enhances system accuracy and efficiency through software-hardware co-design. We deploy edgeSLAM2 on an industrial drone and conduct comprehensive experiments in a large-scale oil field over three months. The results show that edgeSLAM2 surpasses comparative methods by achieving an 80% reduction in bandwidth consumption, a 32% improvement in accuracy, and a 26% reduction in tracking delay.