6 accepted papers
Radar Simulations have become an essential tool in radar algorithm development and testing due to the lack of available high-resolution radar datasets and enormous difficulty in acquiring real-world data. However, simulating radar data is challenging as existing radar simulation tools are not easily
The ability to sense forces is a critical component for ensuring that robots can safely interact with their environment. Yet there are numerous situations, in particular for medical applications, where environmental and sensor density requirements can pose challenges to sensor design. In our previou
A recent spur of interest in indoor robotics has increased the importance of robust simultaneous localization and mapping algorithms in indoor scenarios. This robustness is typically provided by the use of multiple sensors which can correct each others’ deficiencies. In this vein, exteroceptive sens
This paper presents SSLIDE, Sound Source Localization for Indoors using DEep learning, which applies deep neural networks (DNNs) with encoder-decoder structure to localize sound sources with random positions in a continuous space. The spatial features of sound signals received by each microphone are…
Solving depth estimation with monocular cameras enables the possibility of widespread use of cameras as low-cost depth estimation sensors in applications such as autonomous driving and robotics. In order to learn such a scalable depth estimation model, we require a ton of data and labels which are t…
Unsupervised learning for geometric perception (depth, optical flow, etc.) is of great interest to autonomous systems. Recent works on unsupervised learning have made considerable progress on perceiving geometry; however, they usually ignore the coherence of objects and perform poorly under scenario…