Over recent decades, 3D point clouds have been a popular data source applied in automatic
change detection in a wide variety of applications. Compared with 2D images, using 3D …

Point clouds acquired through laser scanning and stereo vision techniques have been
applied in a wide range of applications, proving to be optimal sources for map** 3D urban …

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 …

Scan data of urban environments often include representations of dynamic objects, such as
vehicles, pedestrians, and so forth. However, when it comes to constructing a 3D point cloud …

We present a novel static point cloud map construction algorithm, called Removert, for use
within dynamic urban environments. Leaving only static points and excluding dynamic …

Understanding the scene is key for autonomously navigating vehicles, and the ability to
segment the surroundings online into moving and non-moving objects is a central ingredient …

Large garages are ubiquitous yet intricate scenes that present unique challenges due to
their monotonous colors, repetitive patterns, reflective surfaces, and transparent vehicle …

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

Multi-beam LiDAR sensors, as used on autonomous vehicles and mobile robots, acquire
sequences of 3D range scans (“frames”). Each frame covers the scene sparsely, due to …

Highly dynamic environments, with moving objects such as cars or humans, can pose a
performance challenge for LiDAR SLAM systems that assume largely static scenes. To …