Autonomous driving vehicles require both a precise localization and mapping solution in
different driving environment. In this context, Simultaneous Localization and Mapping …

In this paper, we present BEVerse, a unified framework for 3D perception and prediction
based on multi-camera systems. Unlike existing studies focusing on the improvement of …

Vectorized high-definition map online construction has garnered considerable attention in
the field of autonomous driving research. Most existing approaches model changeable map …

We propose a framework for tightly-coupled lidar inertial odometry via smoothing and
mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory …

Vectorized high-definition map (HD-map) construction, which focuses on the perception of
centimeter-level environmental information, has attracted significant research interest in the …

Constructing HD semantic maps is a central component of autonomous driving. However,
traditional pipelines require a vast amount of human efforts and resources in annotating and …

Odometry is crucial for robot navigation, particularly in situations where global positioning
methods like global positioning system are unavailable. The main goal of odometry is to …

High-definition (HD) semantic maps are a crucial component for autonomous driving on
urban streets. Traditional offline HD maps are created through labor-intensive manual …

We present DeepVCP-a novel end-to-end learning-based 3D point cloud registration
framework that achieves comparable registration accuracy to prior state-of-the-art geometric …

In the realm of autonomous driving, robust perception under out-of-distribution conditions is
paramount for the safe deployment of vehicles. Challenges such as adverse weather …