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This work proposes a first-of-its-kind SLAM architecture fusing anevent-based camera and a Frequency Modulated Continuous Wave (FMCW) radar fordrone navigation. Each sensor is processed by a bio-inspired Spiking NeuralNetwork (SNN) with continual Spike-Timing-Dependent Plasticity (STDP) learning,as observed in the brain. In contrast to most learning-based SLAM systems%,which a) require the acquisition of a representative dataset of the environmentin which navigation must be performed and b) require an off-line trainingphase, our method does not require any offline training phase, but rather theSNN continuously learns features from the input data on the fly via STDP. Atthe same time, the SNN outputs are used as feature descriptors for loop closuredetection and map correction. We conduct numerous experiments to benchmark oursystem against state-of-the-art RGB methods and we demonstrate the robustnessof our DVS-Radar SLAM approach under strong lighting variations.

Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning