Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost
sensors, the easy fusion of other sensors, and richer environmental information. Traditional …

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing
progress over the last few decades and has generated considerable interest in the …

Deep-learning-based localization and mapping approaches have recently emerged as a
new research direction and receive significant attention from both industry and academia …

Global navigation satellite system (GNSS) signals cannot be received indoors, thus to
deploy intelligent vehicles in underground car parks other localization methods are needed …

Autonomous driving services depends on active sensing from modules such as camera,
LiDAR, radar, and communication units. Traditionally, these modules process the sensed …

Due to the limitations of LiDAR, such as its high cost, short service life and massive volume,
visual sensors with their lightweight and low cost are attracting more and more attention and …

Autonomous vehicles (AV) are rapidly becoming integrated into everyday life, with several
countries anticipating their inclusion in public transport networks in the coming years. Safety …

Many studies in the field of robot navigation have focused on environment representation
and localization. The goal of map representation is to summarize spatial information in …

Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors
with different modalities are deployed in end-to-end driving to obtain the global context of …

In autonomous driving applications, accurate and efficient road surface reconstruction is
paramount. This paper introduces RoMe, a novel framework designed for the robust …