Automation is increasingly at the forefront of transportation research, with the potential to
bring fully autonomous vehicles to our roads in the coming years. This comprehensive …

Simultaneous localization and mapping (SLAM) is the process of constructing a global
model of an environment from local observations of it; this is a foundational capability for …

For the last few decades, several major subfields of artificial intelligence including computer
vision, graphics, and robotics have progressed largely independently from each other …

Dynamic radiance field reconstruction methods aim to model the time-varying structure and
appearance of a dynamic scene. Existing methods, however, assume that accurate camera …

We introduce an approach for recovering the 6D pose of multiple known objects in a scene
captured by a set of input images with unknown camera viewpoints. First, we present a …

The assumption of scene rigidity is common in visual SLAM algorithms. However, it limits
their applicability in populated real-world environments. Furthermore, most scenarios …

In this work, we propose an efficient and accurate monocular 3D detection framework in
single shot. Most successful 3D detectors take the projection constraint from the 3D …

Static environment is a prerequisite for most of visual simultaneous localization and
mapping systems. Such a strong assumption limits the practical application of most existing …

Combining Simultaneous Localisation and Mapping (SLAM) estimation and dynamic scene
modelling can highly benefit robot autonomy in dynamic environments. Robot path planning …

Combining multiple LiDARs enables a robot to maximize its perceptual awareness of
environments and obtain sufficient measurements, which is promising for simultaneous …