This article surveys recent progress and discusses future opportunities for simultaneous
localization and map** (SLAM) in extreme underground environments. SLAM in …

Abstract Automated Driving Systems (ADS) open up a new domain for the automotive
industry and offer new possibilities for future transportation with higher efficiency and …

Abstract Spiking Neural Networks (SNNs) provide an energy-efficient deep learning option
due to their unique spike-based event-driven (ie, spike-driven) paradigm. In this paper, we …

The computer vision algorithms used currently in advanced driver assistance systems rely
on image-based RGB cameras, leading to a critical bandwidth–latency trade-off for …

Neuromorphic event-based image sensors capture only the dynamic motion in a scene,
which is then transferred to computation units for motion recognition. This approach …

By mimicking the neurons and synapses of the human brain and employing spiking neural
networks on neuromorphic chips, neuromorphic computing offers a promising energy …

Brain-inspired spiking neural networks (SNNs) are becoming a promising energy-efficient
alternative to traditional artificial neural networks (ANNs). However, the performance gap …

The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …

Abstract We present Recurrent Vision Transformers (RVTs), a novel backbone for object
detection with event cameras. Event cameras provide visual information with sub …

Multimodal fusion can make semantic segmentation more robust. However, fusing an
arbitrary number of modalities remains underexplored. To delve into this problem, we create …