Bio-inspired Multi-scale Visual Place Recognition for the Aerial Vehicle Navigation

Inspired by the discoveries in neuroscience, the method of visual place recognition develops toward using multiple homogenous spatial scales. We present a novel multi-scale place recognition algorithm mimicking the rodent map with multi-scale, discrete and overlapped characteristics. This visual system that can perform place recognition in the aerial environment without any constraint. We present a parallel and multi-channel processing network that can recognize places with a spatial scale and combine the output from these parallel processing channels. This recognizing network can utilize a multi-scale matching that builds associations between robotic activity and places at different spatial scales. Using two aerial datasets, the results demonstrate universal improvements achieved with multi-scale recognition approach. A systematic series of flight simulation experiments are conducted for analyzing the effect on the recognition and localization performance of varying matching scales. Finally, we present insights of further work in robotic navigation.