The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become
important for geospatial data applications. This paper presents a comprehensive review of …

Loop closure detection is of vital importance in the process of simultaneous localization and
mapping (SLAM), as it helps to reduce the cumulative error of the robot's estimated pose and …

Place recognition in 3D data is a challenging task that has been commonly approached by
adapting image-based solutions. Methods based on local features suffer from ambiguity and …

We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar
odometry estimation. Unlike most existing lidar odometry (LO) estimations that go through …

Infrastructure-assisted autonomous driving is an emerging paradigm that expects to
significantly improve the driving safety of autonomous vehicles. The key enabling …

Automatic 3D point cloud registration is a main issue in computer vision and remote sensing.
One of the most commonly adopted solution is the well-known Iterative Closest Point (ICP) …

Environmental perception plays an essential role in autonomous driving tasks and demands
robustness in cluttered dynamic environments such as complex urban scenarios. In this …

This paper presents a new method for urban scene analysis, which comprises 3D point
cloud registration and change detection through fusing Lidar point clouds with significantly …

We introduce a novel method for odometry estimation using convolutional neural networks
from 3D LiDAR scans. The original sparse data are encoded into 2D matrices for the training …

This paper presents a novel method for ground segmentation in Velodyne point clouds. We
propose an encoding of sparse 3D data from the Velodyne sensor suitable for training a …