4 accepted papers
Reliable multimodal sensor fusion algorithms require accurate spatiotemporal calibration. Recently, targetless calibration techniques based on implicit neural representations have proven to provide precise and robust results. Nevertheless, such methods are inherently slow to train given the high com…
In rapidly-evolving domains such as autonomous driving the use of multiple sensors with different modalities is crucial to ensure high operational precision and stability. To correctly exploit the provided information by each sensor in a single common frame it is essential for these sensors to be ac…
With the recent advances in autonomous driving and the decreasing cost of LiDARs, the use of multimodal sensor systems is on the rise. However, in order to make use of the information provided by a variety of complimentary sensors, it is necessary to accurately calibrate them. We take advantage of r…
This paper proposes a visuo-auditory substitution method to assist visually impaired people in scene understanding. Our approach focuses on person localisation in the user’s vicinity in order to ease urban walking. Since a real-time and low-latency is required in this context for user’s security, we…