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 …

Artificial perception for robots operating in outdoor natural environments, including forest
scenarios, has been the object of a substantial amount of research for decades. Regardless …

Simultaneously odometry and mapping using LiDAR data is an important task for mobile
systems to achieve full autonomy in large-scale environments. However, most existing …

Scene flow characterizes the 3D motion between two LiDAR scans captured by an
autonomous vehicle at nearby timesteps. Prevalent methods consider scene flow as point …

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 …

Abstract Simultaneous Localization and Mapping (SLAM) with dense representation plays a
key role in robotics, Virtual Reality (VR), and Augmented Reality (AR) applications. Recent …

Abstract Knowledge about the own pose is key for all mobile robot applications. Thus pose
estimation is part of the core functionalities of mobile robots. Over the last two decades …

Abstract 3D object detection is one of the most important components in any Self-Driving
stack, but current state-of-the-art (SOTA) lidar object detectors require costly & slow manual …

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

Mapping an environment is essential for several robotic tasks, particularly for localization. In
this letter, we address the problem of mapping the environment using LiDAR point clouds …