The ability of intelligent unmanned platforms to achieve autonomous navigation and
positioning in a large-scale environment has become increasingly demanding, in which …

Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of
ground transportation. It is anticipated that ordinary vehicles will one day be replaced with …

Robust and accurate pose estimation of a robotic platform, so-called sensor-based
odometry, is an essential part of many robotic applications. While many sensor odometry …

Semantic scene understanding is important for various applications. In particular, self-driving
cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light …

LiDAR point-cloud segmentation is an important problem for many applications. For large-
scale point cloud segmentation, the de facto method is to project a 3D point cloud to get a …

Reliable and accurate localization and mapping are key components of most autonomous
systems. Besides geometric information about the mapped environment, the semantics …

Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By
sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide …

Simultaneous localization and mapping (SLAM) is a fundamental technique block in the
indoor-navigation system for most autonomous vehicles and robots. SLAM aims at building …

The ability to detect and segment moving objects in a scene is essential for building
consistent maps, making future state predictions, avoiding collisions, and planning. In this …

Multi-beam LiDAR sensors are increasingly used in robotics, particularly with autonomous
cars for localization and perception tasks, both relying on the ability to build a precise map of …