This article presents a collection of multimodal raw data captured from a manned all-terrain
vehicle in the course of two realistic outdoor search and rescue (SAR) exercises for actual …

The ability to develop a high-level understanding of a scene, such as perceiving danger
levels, can prove valuable in planning multi-robot search and rescue (SaR) missions. In this …

With the increasing social demands of disaster response, methods of visual observation for
rescue and safety have become increasingly important. However, because of the shortage …

There has been a plethora of work towards im-proving robot perception and navigation, yet
their application in hazardous environments, like during a fire or an earthquake, is still at a …

The integration of deep learning (DL) technologies into construction offers great potential for
promoting the level of automation in construction. However, the implementation of the DL …

Datasets play a crucial role in the training of deep learning models. For industrial datasets,
the collection and annotation of images and videos is time-consuming, labor-intensive, and …

In this paper we propose an integrated system concept for autonomously surveying and
planning emergency response for areas impacted by natural disasters. Referred to as …

Deep Learning methods for visual tasks have seen significant improvements in accuracy
and resilience. To enhance their performance, many approaches now leverage multiple …

Visual localization is an important task for applications such as navigation and augmented
reality, but is a challenging problem when there are changes in scene appearances through …

Visual localization, the method of self-localization based on camera images, has established
as an additional, GNSS-free technology that is investigated in increasingly real and …