This article surveys recent progress and discusses future opportunities for simultaneous
localization and map** (SLAM) in extreme underground environments. SLAM in …

This study had two main aims:(1) to provide a comprehensive review of terrestrial laser
scanner (TLS) point cloud registration methods and a better understanding of their strengths …

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

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 …

The topic of this review is geometric registration in robotics. Registration algorithms
associate sets of data into a common coordinate system. They have been used extensively …

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 …

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 …

We present an ample description of a socially compliant mobile robotic platform, which is
developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform …

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 …