For intelligent transportation systems, moving object segmentation (MOS) provides valuable
information for robots and intelligent vehicles, such as collision avoidance, path planning …

Semantic perception is a core building block in autonomous driving, since it provides
information about the drivable space and location of other traffic participants. For learning …

Mobile robots that navigate in unknown environments need to be constantly aware of the
dynamic objects in their surroundings for map**, localization, and planning. It is key to …

LiDAR-based scene flow estimation (SFE) and moving object segmentation (MOS) are
important tasks with broad-ranging applications in autonomous driving, such as traffic …

Accurate and robust localization is one of the key modules of autonomous driving and multi-
sensor robotic systems in complex urban scenes. Compared with camera-based …

Using deep learning, 3D autonomous driving semantic segmentation has become a well-
studied subject, with methods that can reach very high performance. Nonetheless, because …

LiDAR-based semantic perception tasks are critical yet challenging for autonomous driving.
Due to the motion of objects and static/dynamic occlusion, temporal information plays an …

Real-time detection of moving objects is an essential capability for robots acting
autonomously in dynamic environments. We thus propose Dynablox, a novel online …

A clean and reliable map of the environment is key for a variety of robotic tasks including
localization, path planning, and navigation. Dynamic objects are an inherent part of our …

The perception of moving objects is crucial for autonomous robots performing collision
avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene …