  Energy-efficient simultaneous localization and mapping (SLAM) is crucial for
mobile robots exploring unknown environments. The mammalian brain solves SLAM
via a network of specialized neurons, exhibiting asynchronous computations and
event-based communications, with very low energy consumption. We propose a
brain-inspired spiking neural network (SNN) architecture that solves the
unidimensional SLAM by introducing spike-based reference frame transformation,
visual likelihood computation, and Bayesian inference. We integrated our
neuromorphic algorithm to Intel's Loihi neuromorphic processor, a non-Von
Neumann hardware that mimics the brain's computing paradigms. We performed
comparative analyses for accuracy and energy-efficiency between our
neuromorphic approach and the GMapping algorithm, which is widely used in small
environments. Our Loihi-based SNN architecture consumes 100 times less energy
than GMapping run on a CPU while having comparable accuracy in head direction
localization and map-generation. These results pave the way for scaling our
approach towards active-SLAM alternative solutions for Loihi-controlled
autonomous robots.
