After several decades of continuously optimizing computing systems, the Moore's law is reaching its end. However, there is an increasing demand for fast and efficient processing systems that can handle large streams of data while decreasing system footprints. Neuromorphic computing answers this need by creating decentralized architectures that communicate with binary events over time. Despite its rapid growth in the last few years, novel algorithms are needed that can leverage the potential of this emerging computing paradigm and can stimulate the design of advanced neuromorphic chips. In this work, we propose a time-based spiking neural network that is mathematically equivalent to the Fourier transform. We implemented the network in the neuromorphic chip Loihi and conducted experiments on five different real scenarios with an automotive frequency modulated continuous wave radar. Experimental results validate the algorithm, and we hope they prompt the design of ad hoc neuromorphic chips that can improve the efficiency of state-of-the-art digital signal processors and encourage research on neuromorphic computing for signal processing.

Localization for mobile robots in dynamic, large-scale environments is a challenging task, especially when relying solely on odometry and 2D LIDAR data. When operating in fleets, mutual detection and the exchange of localization information can be highly valuable. Detecting and classifying different robot types in a heterogeneous fleet, however, is nontrivial with 2D LIDAR data due to the sparse observation information. In this paper a novel approach for mutual robot detection, classification and relative pose estimation based on a combination of convolutional and ConvLSTM layers is presented in order to solve this issue. The algorithm learns an end-to-end classification and pose estimation of robot shapes using 2D LIDAR information transformed into a grid-map. Subsequently a mixture model representing the probability distribution of the pose measurement for each robot type is extracted out of the heatmap output of the network. The output is then used in a cloud-based collaborative localization system in order to increase the localization of the individual robots. The effectiveness of our approach is demonstrated in both, simulation and real-world experiments. The results of our evaluation show that the classification network is able to achieve a precision of 90% on real-world data with an average position estimation error of 14 cm. Moreover, the collaborative localization system is able to increase the localization accuracy of a robot equipped with low-cost sensors by 63%.